Login | Register

Deep Learning Ultrasound Image Analysis: From Classification to Segmentation with Limited Data

Title:

Deep Learning Ultrasound Image Analysis: From Classification to Segmentation with Limited Data

Behboodi, Bahareh (2024) Deep Learning Ultrasound Image Analysis: From Classification to Segmentation with Limited Data. PhD thesis, Concordia University.

[thumbnail of Behboodi_PhD_S2025.pdf]
Preview
Text (application/pdf)
Behboodi_PhD_S2025.pdf - Accepted Version
Available under License Spectrum Terms of Access.
24MB

Abstract

Ultrasound (US) is one of the most widely used imaging modalities in diagnostic and sur- gical settings due to its affordability, safety, and non-invasive nature. However, US images are prone to speckle noise, leading to low resolution and making clinical interpretation chal- lenging. Recently, researchers have applied state-of-the-art deep learning (DL) algorithms from the field of computer vision to the clinical domain. These algorithms require extensive annotated data to achieve meaningful results. In clinical US imaging, however, there are limited available datasets because annotating US images is time-consuming and requires ex- pert radiologists. Additionally, many hospitals restrict data sharing due to patient privacy policies, further limiting the development of DL algorithms for clinical US images. To ad- dress these limitations, this thesis focuses on developing innovative DL algorithms capable of performing with small datasets. Specifically, in Chapter 2, we use simulated US images as an alternative dataset to pre-train a breast tumor segmentation model. We further explore how network design complexity affects segmentation performance with limited data. In Chapter 3, we leverage 2D planes from 3D uterus US scans to develop a segmentation model using data from only 10 cervical cancer patients. In Chapter 4, we create a compact segmentation network with just 0.82 million parameters, applying knowledge distillation to transfer knowl- edge from a well-trained teacher model with 96 million parameters. This approach is ideal for portable US devices, where computational and memory-efficient models are required at the bedside. In Chapter 5, we introduce a novel approach to breast lesion classification by incorporating background as an additional class, improving the detection of invasive ductal carcinomas. In Chapter 6, we develop a framework for detecting quadriceps muscle thickness in US images, an important biomarker for frailty assessment. This framework also provides activation maps, highlighting the model’s focus on either the muscle body or bone surface. The availability of well-annotated datasets for DL model development has been a significant challenge in this thesis. To address this gap, in our final chapter, Chapter 7, we present a publicly available, expert-annotated dataset of intra-operative US images for brain tumor resection—the first of its kind, verified by two expert surgeons. Finally, in Chapter 8, we summarize our findings with concluding remarks and potential future works.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (PhD)
Authors:Behboodi, Bahareh
Institution:Concordia University
Degree Name:Ph. D.
Program:Electrical and Computer Engineering
Date:28 October 2024
Thesis Supervisor(s):Rivaz, Hassan
ID Code:994742
Deposited By: Bahareh Behboodi
Deposited On:18 Mar 2025 15:28
Last Modified:18 Mar 2025 15:28

References:

correct the numbering in these references: [1] Nabila Abraham and Naimul Mefraz Khan. A novel focal tversky loss function with improved attention u-net for lesion segmentation. In 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pages 683–687. IEEE, 2019.
[2] Jonathan Afilalo, Karen P Alexander, Michael J Mack, Mathew S Maurer, Philip Green, Larry A Allen, Jeffrey J Popma, Luigi Ferrucci, and Daniel E Forman. Frailty assessment in the cardiovascular care of older adults. Journal of the American College of Cardiology, 63(8):747–762, 2014.
[3] Jonathan Afilalo, Aayushi Joshi, and Rita Mancini. If you cannot measure frailty, you cannot improve it, 2019.
[4] Mina Amiri, Rupert Brooks, Bahareh Behboodi, and Hassan Rivaz. Two-stage ul- trasound image segmentation using u-net and test time augmentation. International journal of computer assisted radiology and surgery, 15(6):981–988, 2020.
[5] Emran Mohammad Abu Anas, Parvin Mousavi, and Purang Abolmaesumi. A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy. Medical image analysis, 48:107–116, 2018.
[6] Hojat Asgariandehkordi, Sobhan Goudarzi, Adrian Basarab, and Hassan Rivaz. Deep ultrasound denoising using diffusion probabilistic models. In 2023 IEEE International Ultrasonics Symposium (IUS), pages 1–4. IEEE, 2023.
[7] Hojat Asgariandehkordi, Sobhan Goudarzi, Mostafa Sharifzadeh, Adrian Basarab, and Hassan Rivaz. Denoising plane wave ultrasound images using diffusion probabilistic models. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2024.
[8] Jimmy Ba and Rich Caruana. Do deep nets really need to be deep? Advances in neural information processing systems, 27, 2014.
102
[9] Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. Segnet: A deep convo- lutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12):2481–2495, 2017.
[10] Sathiyabhama Balasubramaniam, Yuvarajan Velmurugan, Dhayanithi Jaganathan, and Seshathiri Dhanasekaran. A modified lenet cnn for breast cancer diagnosis in ultrasound images. Diagnostics, 13(17):2746, 2023.
[11] Dhiego Chaves De Almeida Bastos, Parikshit Juvekar, Yanmei Tie, Nick Jowkar, Steve Pieper, Willam M Wells, Wenya Linda Bi, Alexandra Golby, Sarah Frisken, and Tina Kapur. Challenges and opportunities of intraoperative 3d ultrasound with neuronavi- gation in relation to intraoperative mri. Frontiers in Oncology, 11:1463, 2021.
[12] Dhiego Chaves de Almeida Bastos, Parikshit Juvekar, Yanmei Tie, Nick Jowkar, Steve Pieper, William Mercer Wells, Wenya Linda Bi, Alexandra Golby, Sarah Frisken, and Tina Kapur. A clinical perspective on the use of intraoperative 3d ultrasound with neuronavigation and intraoperative mri. Frontiers in Oncology, 11:1463, 2021.
[13] Anton S Becker, Michael Mueller, Elina Stoffel, Magda Marcon, Soleen Ghafoor, and Andreas Boss. Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study. The British journal of radiology, 91 (1083):20170576, 2018.
[14] Bahareh Behboodi and Hassan Rivaz. Ultrasound segmentation using u-net: learning from simulated data and testing on real data. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 6628–6631. IEEE, 2019.
[15] Bahareh Behboodi, Mina Amiri, Rupert Brooks, and Hassan Rivaz. Breast lesion segmentation in ultrasound images with limited annotated data. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pages 1834–1837. IEEE, 2020.
[16] Bahareh Behboodi, Maryse Fortin, Clyde J Belasso, Rupert Brooks, and Hassan Rivaz. Receptive field size as a key design parameter for ultrasound image segmentation with u-net. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pages 2117–2120. IEEE, 2020.
[17] Bahareh Behboodi, Hamze Rasaee, Ali K. Z. Tehrani, and Hassan Rivaz. Deep clas- sification of breast cancer in ultrasound images: more classes, better results with
103

multi-task learning. In Brett C. Byram and Nicole V. Ruiter, editors, Medical Imaging 2021: Ultrasonic Imaging and Tomography, volume 11602, page 116020S. Interna- tional Society for Optics and Photonics, SPIE, 2021. doi: 10.1117/12.2581930. URL https://doi.org/10.1117/12.2581930.
[18] Bahareh Behboodi, Hamze Rasaee, Ali KZ Tehrani, and Hassan Rivaz. Deep classifi- cation of breast cancer in ultrasound images: more classes, better results with multi- task learning. In Medical Imaging 2021: Ultrasonic Imaging and Tomography, volume 11602, page 116020S. International Society for Optics and Photonics, 2021.
[19] Bahareh Behboodi, Hassan Rivaz, Susan Lalondrelle, and Emma Harris. Automatic 3d ultrasound segmentation of uterus using deep learning. In 2021 IEEE International Ultrasonics Symposium (IUS), pages 1–4, 2021. doi: 10.1109/IUS52206.2021.9593671.
[20] Bahareh Behboodi, Rupert Brooks, and Hassan Rivaz. Optimizing knowledge distil- lation for efficient breast ultrasound image segmentation: Insights and performance enhancement. Artificial Intelligence in Health, 2024.
[21] Bahareh Behboodi, Francois-Xavier Carton, Matthieu Chabanas, Sandrine de Rib- aupierre, Ole Solheim, Bodil K. R. Munkvold, Hassan Rivaz, Yiming Xiao, and In- gerid Reinertsen. Open access segmentations of intra-operative brain tumor ultra- sound images. Medical Physics, 2024. ISSN 2076-3417. doi: 10.1002/mp.17317. URL https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.17317.
[22] Bahareh Behboodi, Jeremy Obrand, Jonathan Afilalo, and Hassan Rivaz. Deepsarc-us: A deep learning framework for assessing sarcopenia using ultrasound images. Applied Sciences, 14(15), 2024. ISSN 2076-3417. doi: 10.3390/app14156726. URL https: //www.mdpi.com/2076-3417/14/15/6726.
[23] Clyde J Belasso, Bahareh Behboodi, Habib Benali, Mathieu Boily, Hassan Rivaz, and Maryse Fortin. Luminous database: lumbar multifidus muscle segmentation from ul- trasound images. BMC Musculoskeletal Disorders, 21(1):1–11, 2020.
[24] Yoshua Bengio, J ́eroˆme Louradour, Ronan Collobert, and Jason Weston. Curricu- lum learning. In Proceedings of the 26th annual international conference on machine learning, pages 41–48, 2009.
[25] Peng Bian, Xiyu Zhang, Ruihong Liu, Huijie Li, Qingqing Zhang, and Baoling Dai. Deep-learning-based color doppler ultrasound image feature in the diagnosis of elderly
104

patients with chronic heart failure complicated with sarcopenia. Journal of Healthcare Engineering, 2021, 2021.
[26] Lior Bibas, Eli Saleh, Samah Al-Kharji, Jessica Chetrit, Louis Mullie, Marcelo Can- tarovich, Renzo Cecere, Nadia Giannetti, and Jonathan Afilalo. Muscle mass and mortality after cardiac transplantation. Transplantation, 102(12):2101–2107, 2018.
[27] Paul Blanc-Durand, J-B Schiratti, Kathryn Schutte, Paul Jehanno, Paul Herent, Fr ́ed ́eric Pigneur, Olivier Lucidarme, Y Benaceur, Alexandre Sadate, Alain Luciani, et al. Abdominal musculature segmentation and surface prediction from ct using deep learning for sarcopenia assessment. Diagnostic and Interventional Imaging, 101(12): 789–794, 2020.
[28] Alexander Buslaev, Vladimir I Iglovikov, Eugene Khvedchenya, Alex Parinov, Mikhail Druzhinin, and Alexandr A Kalinin. Albumentations: fast and flexible image augmen- tations. Information, 11(2):125, 2020.
[29] Michal Byra, Michael Galperin, Haydee Ojeda-Fournier, Linda Olson, Mary O’Boyle, Christopher Comstock, and Michael Andre. Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion. Medical physics, 46(2):746–755, 2019.
[30] Luca Canalini, Jan Klein, Dorothea Miller, and Ron Kikinis. Segmentation-based registration of ultrasound volumes for glioma resection in image-guided neurosurgery. International journal of computer assisted radiology and surgery, 14(10):1697–1713, 2019.
[31] Luca Canalini, Jan Klein, Dorothea Miller, and Ron Kikinis. Enhanced registration of ultrasound volumes by segmentation of resection cavity in neurosurgical procedures. International Journal of Computer Assisted Radiology and Surgery, 15(12):1963–1974, 2020.
[32] Zhantao Cao, Guowu Yang, Qin Chen, Xiaolong Chen, and Fengmao Lv. Breast tumor classification through learning from noisy labeled ultrasound images. Medical Physics, 47(3):1048–1057, 2020.
[33] Fran ̧cois-Xavier Carton, Matthieu Chabanas, Florian Le Lann, and Jack H. Noble. Automatic segmentation of brain tumor resections in intraoperative ultrasound images
105

using U-Net. Journal of Medical Imaging, 7(3):1 – 15, 2020. doi: 10.1117/1.JMI.7.3. 031503.
[34] Ruey-Feng Chang, Kuang-Che Chang-Chien, Hao-Jen Chen, Dar-Ren Chen, Etsuo Takada, and Woo Kyung Moon. Whole breast computer-aided screening using free- hand ultrasound. In International Congress Series, volume 1281, pages 1075–1080. Elsevier, 2005.
[35] Hila Chefer, Shir Gur, and Lior Wolf. Transformer interpretability beyond attention visualization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 782–791, 2021.
[36] Changhao Chen, Bing Wang, Chris Xiaoxuan Lu, Niki Trigoni, and Andrew Markham. A survey on deep learning for localization and mapping: Towards the age of spatial machine intelligence. arXiv preprint arXiv:2006.12567, 2020.
[37] Chung-Ming Chen, Yi-Hong Chou, Ko-Chung Han, Guo-Shian Hung, Chui-Mei Tiu, Hong-Jen Chiou, and See-Ying Chiou. Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks. Ra- diology, 226(2):504–514, 2003.
[38] Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Re- thinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017.
[39] Zhen Chen, Xiaoqing Guo, Peter YM Woo, and Yixuan Yuan. Super-resolution en- hanced medical image diagnosis with sample affinity interaction. IEEE Transactions on Medical Imaging, 40(5):1377–1389, 2021.
[40] Heng-Da Cheng, Juan Shan, Wen Ju, Yanhui Guo, and Ling Zhang. Automated breast cancer detection and classification using ultrasound images: A survey. Pattern recognition, 43(1):299–317, 2010.
[41] Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282, 2017.
[42] Tsung-Chen Chiang, Yao-Sian Huang, Rong-Tai Chen, Chiun-Sheng Huang, and Ruey- Feng Chang. Tumor detection in automated breast ultrasound using 3-d cnn and prioritized candidate aggregation. IEEE transactions on medical imaging, 38(1):240– 249, 2019.
[43] Jui-Ying Chiao, Kuan-Yung Chen, Ken Ying-Kai Liao, Po-Hsin Hsieh, Geoffrey Zhang, and Tzung-Chi Huang. Detection and classification the breast tumors using mask r-cnn on sonograms. Medicine, 98(19), 2019.
[44] Kyunghyun Cho. Learning phrase representations using rnn encoder-decoder for sta- tistical machine translation. arXiv preprint arXiv:1406.1078, 2014.
[45] Sophie Church, Emily Rogers, Kenneth Rockwood, and Olga Theou. A scoping review of the clinical frailty scale. BMC geriatrics, 20:1–18, 2020.
[46] Paul A Cohen, Anjua Jhingran, Ana Oaknin, and Lynette Denny. Cervical cancer. The Lancet, 393(10167):169–182, 2019.
[47] Marly Guimara ̃es Fernandes Costa, Joa ̃o Paulo Mendes Campos, Gustavo de Aquino e Aquino, Wagner Coelho de Albuquerque Pereira, and C ́ıcero Ferreira Fernandes Costa Filho. Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in us images. BMC Medical Imaging, 19(1):1–13, 2019.
[48] AJ Cruz-Jentoft and J-P Michel. Sarcopenia: a useful paradigm for physical frailty. European Geriatric Medicine, 4(2):102–105, 2013.
[49] Alfonso J Cruz-Jentoft, Gu ̈listan Bahat, Ju ̈rgen Bauer, Yves Boirie, Olivier Bruy`ere, Tommy Cederholm, Cyrus Cooper, Francesco Landi, Yves Rolland, Avan Aihie Sayer, et al. Sarcopenia: revised european consensus on definition and diagnosis. Age and ageing, 48(1):16–31, 2019.
[50] Abdulla A Damluji, Daniel E Forman, Sean Van Diepen, Karen P Alexander, Robert L Page, Scott L Hummel, Venu Menon, Jason N Katz, Nancy M Albert, Jonathan Afilalo, et al. Older adults in the cardiac intensive care unit: factoring geriatric syndromes in the management, prognosis, and process of care: a scientific statement from the american heart association. Circulation, 141(2):e6–e32, 2020.
[51] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009.
[52] Jacob Devlin. Bert: Pre-training of deep bidirectional transformers for language un- derstanding. arXiv preprint arXiv:1810.04805, 2018.
[53] KT Dilna and D Jude Hemanth. Fibroid detection in ultrasound uterus images using image processing. In International Conference on Innovative Computing and Commu- nications, pages 173–179. Springer, 2020.
[54] Jianrui Ding, Heng-Da Cheng, Jianhua Huang, Jiafeng Liu, and Yingtao Zhang. Breast ultrasound image classification based on multiple-instance learning. Journal of digital imaging, 25(5):620–627, 2012.
[55] Wanli Ding, Heye Zhang, Shuxin Zhuang, Zhemin Zhuang, and Zhifan Gao. Multi- view stereoscopic attention network for 3d tumor classification in automated breast ultrasound. Expert Systems with Applications, 234:120969, 2023.
[56] Xuehai Ding, Yanting Liu, Junjuan Zhao, Ren Wang, Chengfan Li, Quanyong Luo, and Chentian Shen. A novel wavelet-transform-based convolution classification network for cervical lymph node metastasis of papillary thyroid carcinoma in ultrasound images. Computerized Medical Imaging and Graphics, 109:102298, 2023.
[57] Richard Dodds and Avan Aihie Sayer. Sarcopenia and frailty: new challenges for clinical practice. Clinical medicine, 16(5):455, 2016.
[58] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
[59] Qi Dou, Quande Liu, Pheng Ann Heng, and Ben Glocker. Unpaired multi-modal segmentation via knowledge distillation. IEEE transactions on medical imaging, 39 (7):2415–2425, 2020.
[60] J Egger, T Kapur, A Fedorov, S Pieper, JV Miller, H Veeraraghavan, B Freisleben, AJ Golby, C Nimsky, and R Kikinis. Gbm volumetry using the 3d slicer medical image computing platform. sci rep 3: 1364, 2013.
[61] Jianan Fan, Dongnan Liu, Hang Chang, Heng Huang, Mei Chen, and Weidong Cai. Taxonomy adaptive cross-domain adaptation in medical imaging via optimization tra- jectory distillation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 21174–21184, 2023.[62] Andriy Fedorov, Reinhard Beichel, Jayashree Kalpathy-Cramer, Julien Finet, Jean- Christophe Fillion-Robin, Sonia Pujol, Christian Bauer, Dominique Jennings, Fiona Fennessy, Milan Sonka, et al. 3d slicer as an image computing platform for the quan- titative imaging network. Magnetic resonance imaging, 30(9):1323–1341, 2012.
[63] Rosie Fountotos, Haroon Munir, Michael Goldfarb, Sandra Lauck, Dae Kim, Louis Perrault, Rakesh Arora, Emmanuel Moss, Lawrence G Rudski, Melissa Bendayan, et al. Prognostic value of handgrip strength in older adults undergoing cardiac surgery. Canadian Journal of Cardiology, 2021.
[64] Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3146–3154, 2019.
[65] Tomoyuki Fujioka, Kazunori Kubota, Jen Feng Hsu, Ruey Feng Chang, Terumasa Sawada, Yoshimi Ide, Kanae Taruno, Meishi Hankyo, Tomoko Kurita, Seigo Nakamura, et al. Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound. Journal of Medical Ultrasonics, 50(4): 511–520, 2023.
[66] Chengling Gao, Hailiang Ye, Feilong Cao, Chenglin Wen, Qinghua Zhang, and Feng Zhang. Multiscale fused network with additive channel–spatial attention for image segmentation. Knowledge-Based Systems, 214:106754, 2021.
[67] Zhifan Gao, Jonathan Chung, Mohamed Abdelrazek, Stephanie Leung, William Kongto Hau, Zhanchao Xian, Heye Zhang, and Shuo Li. Privileged modality distillation for vessel border detection in intracoronary imaging. IEEE transactions on medical imaging, 39(5):1524–1534, 2019.
[68] Ian J. Gerard, Marta Kersten-Oertel, Jeffery A. Hall, Denis Sirhan, and D. Louis Collins. Brain shift in neuronavigation of brain tumors: An updated review of intra- operative ultrasound applications. Frontiers in Oncology, 10, 2021. ISSN 2234-943X. doi: 10.3389/fonc.2020.618837. URL https://www.frontiersin.org/articles/10. 3389/fonc.2020.618837.
[69] Behnaz Gheflati and Hassan Rivaz. Vision transformers for classification of breast ultrasound images. In 2022 44th Annual International Conference of the IEEE Engi- neering in Medicine & Biology Society (EMBC), pages 480–483. IEEE, 2022.
[70] Dipannita Ghosh, Amish Kumar, Palash Ghosal, Tamal Chowdhury, Anup Sadhu, and Debashis Nandi. Breast lesion segmentation in ultrasound images using deep convolutional neural networks. In 2020 IEEE Calcutta Conference (CALCON), pages 318–322. IEEE, 2020.
[71] Walter Go ́mez, Wagner Coelho Albuquerque Pereira, and Antonio Fernando C Infan- tosi. Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound. IEEE transactions on medical imaging, 31(10):1889– 1899, 2012.
[72] Yunchao Gong, Liu Liu, Ming Yang, and Lubomir Bourdev. Compressing deep con- volutional networks using vector quantization. arXiv preprint arXiv:1412.6115, 2014.
[73] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Ad- vances in neural information processing systems, 27, 2014.
[74] Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. Knowledge distillation: A survey. International Journal of Computer Vision, 129(6):1789–1819, 2021.
[75] Sobhan Goudarzi and Hassan Rivaz. Deep reconstruction of high-quality ultrasound images from raw plane-wave data: A simulation and in vivo study. Ultrasonics, 125: 106778, 2022.
[76] Sobhan Goudarzi, Amir Asif, and Hassan Rivaz. Multi-focus ultrasound imaging us- ing generative adversarial networks. In 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pages 1118–1121. IEEE, 2019.
[77] Sobhan Goudarzi, Amir Asif, and Hassan Rivaz. Fast multi-focus ultrasound image recovery using generative adversarial networks. IEEE Transactions on Computational Imaging, 6:1272–1284, 2020.
[78] Sobhan Goudarzi, Adrian Basarab, and Hassan Rivaz. Inverse problem of ultrasound beamforming with denoising-based regularized solutions. IEEE Transactions on Ul- trasonics, Ferroelectrics, and Frequency Control, 69(10):2906–2916, 2022.
[79] Sobhan Goudarzi, Adrian Basarab, and Hassan Rivaz. A unifying approach to in- verse problems of ultrasound beamforming and deconvolution. IEEE Transactions on Computational Imaging, 9:197–209, 2023.
[80] Sobhan Goudarzi, Jesse Whyte, Mathieu Boily, Anna Towers, Robert D Kilgour, and Hassan Rivaz. Segmentation of arm ultrasound images in breast cancer-related lym- phedema: A database and deep learning algorithm. IEEE Transactions on Biomedical Engineering, 70(9):2552–2563, 2023.
[81] Holly Gwyther, Rachel Shaw, Eva-Amparo Jaime Dauden, Barbara D’Avanzo, Donata Kurpas, Maria Bujnowska-Fedak, Tomasz Kujawa, Maura Marcucci, Antonio Cano, and Carol Holland. Understanding frailty: a qualitative study of european healthcare policy-makers’ approaches to frailty screening and management. BMJ open, 8(1): e018653, 2018.
[82] M Halliwell. A tutorial on ultrasonic physics and imaging techniques. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 224(2):127–142, 2010.
[83] Seokmin Han, Ho-Kyung Kang, Ja-Yeon Jeong, Moon-Ho Park, Wonsik Kim, Won- Chul Bang, and Yeong-Kyeong Seong. A deep learning framework for supporting the classification of breast lesions in ultrasound images. Physics in Medicine & Biology, 62(19):7714, 2017.
[84] Stephen Hanson and Lorien Pratt. Comparing biases for minimal network construction with back-propagation. Advances in neural information processing systems, 1, 1988.
[85] Hoda S Hashemi, Stefanie Fallone, Mathieu Boily, Anna Towers, Robert D Kilgour, and Hassan Rivaz. Assessment of mechanical properties of tissue in breast cancer- related lymphedema using ultrasound elastography. IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 66(3):541–550, 2018.
[86] Mohamed A Hassanien, Vivek Kumar Singh, Domenec Puig, and Mohamed Abdel- Nasser. Predicting breast tumor malignancy using deep convnext radiomics and quality-based score pooling in ultrasound sequences. Diagnostics, 12(5):1053, 2022.
[87] Babak Hassibi and David Stork. Second order derivatives for network pruning: Optimal brain surgeon. Advances in neural information processing systems, 5, 1992.
[88] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision, pages 1026–1034, 2015.
[89] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
[90] Kaiming He, Georgia Gkioxari, Piotr Dolla ́r, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961–2969, 2017.
[91] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll ́ar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16000–16009, 2022.
[92] Tong He, Chunhua Shen, Zhi Tian, Dong Gong, Changming Sun, and Youliang Yan. Knowledge adaptation for efficient semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 578–587, 2019.
[93] Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He, and Paul Kennedy. Deep learning techniques for medical image segmentation: achievements and challenges. Journal of digital imaging, 32(4):582–596, 2019.
[94] Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2(7), 2015.
[95] Thi Kieu Khanh Ho and Jeonghwan Gwak. Utilizing knowledge distillation in deep learning for classification of chest x-ray abnormalities. IEEE Access, 8:160749–160761, 2020.
[96] Emiel O Hoogendijk, Jonathan Afilalo, Kristine E Ensrud, Paul Kowal, Graziano Onder, and Linda P Fried. Frailty: implications for clinical practice and public health. The Lancet, 394(10206):1365–1375, 2019.
[97] Andrew Howard, Andrey Zhmoginov, Liang-Chieh Chen, Mark Sandler, and Menglong Zhu. Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. In Proc. CVPR, pages 4510–4520, 2018.
[98] Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al. Searching for
mobilenetv3. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1314–1324, 2019.
[99] Yi-Hsuan Hsiao, Shun-Fa Yang, Ya-Hui Chen, Tze-Ho Chen, Horng-Der Tsai, Ming- Chih Chou, and Pang-Hsin Chou. Updated applications of ultrasound in uterine cer- vical cancer. Journal of Cancer, 12(8):2181, 2021.
[100] Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132–7141, 2018.
[101] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708, 2017.
[102] Haibo Huang, Haobo Chen, Haohao Xu, Ying Chen, Qihui Yu, Yehua Cai, and Qi Zhang. Cross-tissue/organ transfer learning for the segmentation of ultrasound images using deep residual u-net. Journal of Medical and Biological Engineering, 41 (2):137–145, 2021.
[103] Yunzhi Huang, Luyi Han, Haoran Dou, Honghao Luo, Zhen Yuan, Qi Liu, Jiang Zhang, and Guangfu Yin. Two-stage cnns for computerized bi-rads categorization in breast ultrasound images. Biomedical engineering online, 18(1):1–18, 2019.
[104] Sumaira Hussain, Xiaoming Xi, Inam Ullah, Yongjian Wu, Chunxiao Ren, Zhao Lianzheng, Cuihuan Tian, and Yilong Yin. Contextual level-set method for breast tumor segmentation. IEEE Access, 8:189343–189353, 2020.
[105] Pavel Iakubovskii. Segmentation models pytorch. https://github.com/qubvel/ segmentation_models.pytorch, 2019.
[106] Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox. Flownet 2.0: Evolution of optical flow estimation with deep networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2462–2470, 2017.
[107] Elisee Ilunga-Mbuyamba, Juan Gabriel Avina-Cervantes, Dirk Lindner, Felix Arlt, Jean Fulbert Ituna-Yudonago, and Claire Chalopin. Patient-specific model-based segmentation of brain tumors in 3d intraoperative ultrasound images. International journal of computer assisted radiology and surgery, 13(3):331–342, 2018.
[108] Fabian Isensee, Jens Petersen, Andre Klein, David Zimmerer, Paul F Jaeger, Simon Kohl, Jakob Wasserthal, Gregor Koehler, Tobias Norajitra, Sebastian Wirkert, et al. nnu-net: Self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:1809.10486, 2018.
[109] Fabian Isensee, Paul F Jaeger, Simon AA Kohl, Jens Petersen, and Klaus H Maier-Hein. nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2):203–211, 2021.
[110] Noushin Jafarpisheh, Timothy J Hall, Hassan Rivaz, and Ivan M Rosado-Mendez. Analytic global regularized backscatter quantitative ultrasound. IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 68(5):1605–1617, 2020.
[111] Jørgen Arendt Jensen. Field: A program for simulating ultrasound systems. In 10TH Nordicbaltic Conference on Biomedical Imaging, VOL. 4, Supplement 1, Part 1: 351–353. Citeseer, 1996.
[112] Jørgen Arendt Jensen and Niels Bruun Svendsen. Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers. IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 39(2):262–267, 1992.
[113] Aayushi Joshi, Rita Mancini, Stephan Probst, Gad Abikhzer, Yves Langlois, Jean-Francois Morin, Lawrence G Rudski, and Jonathan Afilalo. Sarcopenia in cardiac surgery: Dual x-ray absorptiometry study from the mcgill frailty registry. American Heart Journal, 2021.
[114] Parikshit Juvekar, Reuben Dorent, Fryderyk K¨ogl, Erickson Torio, Colton Barr, Laura Rigolo, Colin Galvin, Nick Jowkar, Anees Kazi, Nazim Haouchine, et al. Remind: The brain resection multimodal imaging database. medRxiv, 2023.
[115] Kyungsang Kim, Fabiola Macruz, Dufan Wu, Christopher Bridge, Suzannah McKinney, Ahad Alhassan Al Saud, Elshaimaa Sharaf, Adam Pely, Paul Danset, Tom Duffy, et al. Point-of-care ai-assisted stepwise ultrasound pneumothorax diagnosis. Physics in Medicine & Biology, 68(20):205013, 2023.
[116] DP Kingma and J Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[117] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. "Segment anything." In *Proceedings of the IEEE/CVF International Conference on Computer Vision*, pages 4015–4026, 2023.

[118] Gotaro Kojima, Steve Iliffe, and Kate Walters. "Frailty index as a predictor of mortality: a systematic review and meta-analysis." *Age and Ageing*, 47(2):193–200, 2018.

[119] Terry K Koo and Mae Y Li. "A guideline of selecting and reporting intraclass correlation coefficients for reliability research." *Journal of Chiropractic Medicine*, 15(2):155–163, 2016.

[120] Viksit Kumar, Jeremy M Webb, Adriana Gregory, Max Denis, Duane D Meixner, Mahdi Bayat, Dana H Whaley, Mostafa Fatemi, and Azra Alizad. "Automated and real-time segmentation of suspicious breast masses using convolutional neural network." *PloS One*, 13(5):e0195816, 2018.

[121] Ali KZ Tehrani, Morteza Mirzaei, and Hassan Rivaz. "Semi-supervised training of optical flow convolutional neural networks in ultrasound elastography." In *International Conference on Medical Image Computing and Computer-Assisted Intervention*, pages 504–513. Springer, 2020.

[122] Sarah Leclerc, Erik Smistad, Joao Pedrosa, Andreas Østvik, Frederic Cervenansky, Florian Espinosa, Torvald Espeland, Erik Andreas Rye Berg, Pierre-Marc Jodoin, Thomas Grenier, et al. "Deep learning for segmentation using an open large-scale dataset in 2D echocardiography." *IEEE Transactions on Medical Imaging*, 38(9):2198–2210, 2019.

[123] Yann LeCun, John Denker, and Sara Solla. "Optimal brain damage." *Advances in Neural Information Processing Systems*, 2, 1989.

[124] Haeyun Lee, Jinhyoung Park, and Jae Youn Hwang. "Channel attention module with multiscale grid average pooling for breast cancer segmentation in an ultrasound image." *IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control*, 67(7):1344–1353, 2020.

[125] Kyungsu Lee, Haeyun Lee, Georges El Fakhri, Jonghye Woo, and Jae Youn Hwang. "Self-supervised domain adaptive segmentation of breast cancer via test-time fine-tuning." In *International Conference on Medical Image Computing and Computer-Assisted Intervention*, pages 539–550. Springer, 2023.

[126] Seung Hoo Lee and Hyun Sik Gong. "Measurement and interpretation of handgrip strength for research on sarcopenia and osteoporosis." *Journal of Bone Metabolism*, 27(2):85, 2020.

[127] Jun Li, Junyu Chen, Yucheng Tang, Ce Wang, Bennett A Landman, and S Kevin Zhou. "Transforming medical imaging with transformers? A comparative review of key properties, current progresses, and future perspectives." *Medical Image Analysis*, page 102762, 2023.

[128] Kang Li, Lequan Yu, Shujun Wang, and Pheng-Ann Heng. "Towards cross-modality medical image segmentation with online mutual knowledge distillation." In *Proceedings of the AAAI Conference on Artificial Intelligence*, volume 34, pages 775–783, 2020.

[129] Lele Li, Ziling Wu, Juan Liu, Lang Wang, Yu Jin, Peng Jiang, Jing Feng, and Meng Wu. "Cross-attention based multi-scale feature fusion vision transformer for breast ultrasound image classification." In *2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)*, pages 1616–1619. IEEE, 2022.

[130] Zeju Li, Konstantinos Kamnitsas, and Ben Glocker. "Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation." In *International Conference on Medical Image Computing and Computer-Assisted Intervention*, pages 402–410. Springer, 2019.

[131] Yuanhao Liang, Ran He, Yongshuai Li, and Zhili Wang. "Simultaneous segmentation and classification of breast lesions from ultrasound images using mask R-CNN." In *2019 IEEE International Ultrasonics Symposium (IUS)*, pages 1470–1472. IEEE, 2019.

[132] David Liljequist, Britt Elfving, and Kirsti Skavberg Roaldsen. "Intraclass correlation–a discussion and demonstration of basic features." *PloS One*, 14(7):e0219854, 2019.

[133] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. "Focal loss for dense object detection." In *Proceedings of the IEEE International Conference on Computer Vision*, pages 2980–2988, 2017.

[134] Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez. "A survey on deep learning in medical image analysis." *Medical Image Analysis*, 42:60–88, 2017.

[135] Xilun Liu and Mohamed Almekkawy. "Ultrasound super resolution using vision transformer with convolution projection operation." In *2022 IEEE International Ultrasonics Symposium (IUS)*, pages 1–4. IEEE, 2022.

[136] Yifan Liu, Ke Chen, Chris Liu, Zengchang Qin, Zhenbo Luo, and Jingdong Wang. "Structured knowledge distillation for semantic segmentation." In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pages 2604–2613, 2019.

[137] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. "Swin transformer: Hierarchical vision transformer using shifted windows." In *Proceedings of the IEEE/CVF International Conference on Computer Vision*, pages 10012–10022, 2021.

[138] Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, et al. "Swin transformer v2: Scaling up capacity and resolution." In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pages 12009–12019, 2022.

[139] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. "A convnet for the 2020s." In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pages 11976–11986, 2022.

[140] Jonathan Long, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 3431–3440, 2015.

[141] Jonathan L Long, Ning Zhang, and Trevor Darrell. "Do convnets learn correspondence?" In *Advances in Neural Information Processing Systems*, pages 1601–1609, 2014.

[142] Ian Loram, Abdul Siddique, María B Sánchez, Pete Harding, Monty Silverdale, Christopher Kobylecki, and Ryan Cunningham. "Objective analysis of neck muscle boundaries for cervical dystonia using ultrasound imaging and deep learning." *IEEE Journal of Biomedical and Health Informatics*, 24(4):1016–1027, 2020.

[143] Meng Lou, Jie Meng, Yunliang Qi, Xiaorong Li, and Yide Ma. "Mcrnet: Multi-level context refinement network for semantic segmentation in breast ultrasound imaging." *Neurocomputing*, 470:154–169, 2022.

[144] David N. Louis, Arie Perry, Pieter Wesseling, Daniel J. Brat, Ian A. Cree, Dominique Figarella-Branger, Cynthia Hawkins, H.K. Ng, Stefan M. Pfister, Guido Reifenberger, Riccardo Soffietti, Andreas von Deimling, and David W. Ellison. "The 2021 WHO Classification of Tumors of the Central Nervous System: A Summary." *Neuro-Oncology*, 23(8):1231–1251, 2021. doi: 10.1093/neuonc/noab106.

[145] Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel. "Understanding the Effective Receptive Field in Deep Convolutional Neural Networks." In *Advances in Neural Information Processing Systems*, pages 4898–4906, 2016.

[146] Karttikeya Mangalam and Mathieu Salzamann. "On Compressing U-Net Using Knowledge Distillation." *arXiv preprint arXiv:1812.00249*, 2018.

[147] Francesco Marzola, Nens van Alfen, Jonne Doorduin, and Kristen M. Meiburger. "Deep Learning Segmentation of Transverse Musculoskeletal Ultrasound Images for Neuromuscular Disease Assessment." *Computers in Biology and Medicine*, 135:104623, 2021.

[148] Sarah A. Mason, Ingrid M. White, Susan Lalondrelle, Jeffrey C. Bamber, and Emma J. Harris. "The Stacked-Ellipse Algorithm: An Ultrasound-Based 3-D Uterine Segmentation Tool for Enabling Adaptive Radiotherapy for Uterine Cervix Cancer." *Ultrasound in Medicine & Biology*, 46(4):1040–1052, 2020.

[149] Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BraTS)." *IEEE Transactions on Medical Imaging*, 34(10):1993–2024, 2014.

[150] Laurence Mercier, Rolando F. Del Maestro, Kevin Petrecca, David Araujo, Claire Haegelen, and D. Louis Collins. "Online Database of Clinical MR and Ultrasound Images of Brain Tumors." *Medical Physics*, 39(6 Part 1):3253–3261, 2012.

[151] Oleg V. Michailovich and Allen Tannenbaum. "Despeckling of Medical Ultrasound Images." *IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control*, 53(1):64–78, 2006.

[152] Fausto Milletari, Seyed-Ahmad Ahmadi, Christine Kroll, Annika Plate, Verena E. Rozanski, Juliana Maiostre, Johannes Levin, Olaf Dietrich, Birgit Ertl-Wagner, Kai Bötzel, and Nassir Navab. "Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound." *CoRR*, abs/1601.07014, 2016. URL http://arxiv.org/abs/1601.07014.

[153] Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation." In *2016 Fourth International Conference on 3D Vision (3DV)*, pages 565–571. IEEE, 2016.

[154] Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation." In *2016 Fourth International Conference on 3D Vision (3DV)*, pages 565–571. IEEE, 2016.

[155] Mehdi Mirza and Simon Osindero. "Conditional Generative Adversarial Nets." *arXiv preprint arXiv:1411.1784*, 2014.

[156] Woo Kyung Moon, Yan-Wei Lee, Hao-Hsiang Ke, Su Hyun Lee, Chiun-Sheng Huang, and Ruey-Feng Chang. "Computer-Aided Diagnosis of Breast Ultrasound Images Using Ensemble Learning from Convolutional Neural Networks." *Computer Methods and Programs in Biomedicine*, 190:105361, 2020.

[157] Christopher L. Moore and Joshua A. Copel. "Point-of-Care Ultrasonography." *New England Journal of Medicine*, 364(8):749–757, 2011.

[158] John E. Morley, Bruno Vellas, G. Abellan Van Kan, Stefan D. Anker, Juergen M. Bauer, Roberto Bernabei, Matteo Cesari, W.C. Chumlea, Wolfram Doehner, Jonathan Evans, et al. "Frailty Consensus: A Call to Action." *Journal of the American Medical Directors Association*, 14(6):392–397, 2013.

[159] Rand Muhtaseb and Mohammad Yaqub. "Echocotr: Estimation of the Left Ventricular Ejection Fraction from Spatiotemporal Echocardiography." In *Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference*, Singapore, September 18–22, 2022, Proceedings, Part IV, pages 370–379. Springer, 2022.

[160] Bodil Karoline Ravn Munkvold, Hans Kristian Bø, Asgeir Store Jakola, Ingerid Reinertsen, Erik Magnus Berntsen, Geirmund Unsgård, Sverre Helge Torp, and Ole Solheim. "Tumor Volume Assessment in Low-Grade Gliomas: A Comparison of Preoperative Magnetic Resonance Imaging to Co-Registered Intraoperative 3-Dimensional Ultrasound Recordings." *Neurosurgery*, 83(2):288–296, 2018. doi: 10.1093/neuros/nyx392.

[161] Arya Nabavi, Peter McL. Black, David T. Gering, Carl-Fredrik Westin, Vivek Mehta, Richard S. Pergolizzi Jr., Mathieu Ferrant, Simon K. Warfield, Nobuhiko Hata, Richard B. Schwartz, et al. "Serial Intraoperative Magnetic Resonance Imaging of Brain Shift." *Neurosurgery*, 48(4):787–798, 2001.

[162] Vinod Nair and Geoffrey E. Hinton. "Rectified Linear Units Improve Restricted Boltzmann Machines." In *Proceedings of the 27th International Conference on Machine Learning (ICML-10)*, pages 807–814, 2010.

[163] Ana I. L. Namburete, Weidi Xie, Mohammad Yaqub, Andrew Zisserman, and J. Alison Noble. "Fully-Automated Alignment of 3D Fetal Brain Ultrasound to a Canonical Reference Space Using Multi-Task Learning." *Medical Image Analysis*, 46:1–14, 2018. ISSN 1361-8415. doi: https://doi.org/10.1016/j.media.2018.02.006. URL: https://www.sciencedirect.com/science/article/pii/S1361841518300306.

[164] Sumanth Nandamuri, Debarghya China, Pabitra Mitra, and Debdoot Sheet. "Sumnet: Fully Convolutional Model for Fast Segmentation of Anatomical Structures in Ultrasound Volumes." *arXiv preprint arXiv:1901.06920*, 2019.

[165] James O'Neill. "An Overview of Neural Network Compression." *arXiv preprint arXiv:2006.03669*, 2020.

[166] Zhenyuan Ning, Ke Wang, Shengzhou Zhong, Qianjin Feng, and Yu Zhang. "CF2-Net: Coarse-to-Fine Fusion Convolutional Network for Breast Ultrasound Image Segmentation." *arXiv preprint arXiv:2003.10144*, 2020.

[167] Alison Noble and Djamal Boukerroui. "Ultrasound Image Segmentation: A Survey." *IEEE Transactions on Medical Imaging*, 25(8):987–1010, 2006.

[168] J. Alison Noble and Djamal Boukerroui. "Ultrasound Image Segmentation: A Survey." *IEEE Transactions on Medical Imaging*, 25(8):987–1010, 2006.

[169] Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y. Hammerla, Bernhard Kainz, et al. "Attention U-Net: Learning Where to Look for the Pancreas." *arXiv preprint arXiv:1804.03999*, 2018.

[170] Antonio Omuro and Lisa M. DeAngelis. "Glioblastoma and Other Malignant Gliomas: A Clinical Review." *JAMA*, 310(17):1842–1850, 2013.

[171] Sonia H. Contreras Ortiz, Tsuicheng Chiu, and Martin D. Fox. "Ultrasound Image Enhancement: A Review." *Biomedical Signal Processing and Control*, 7(5):419–428, 2012.

[172] Abdeldjalil Ouahabi and Abdelmalik Taleb-Ahmed. "Deep Learning for Real-Time Semantic Segmentation: Application in Ultrasound Imaging." *Pattern Recognition Letters*, 144:27–34, 2021.

[173] Julia P. Owen, Marian Blazes, Niranchana Manivannan, Gary C. Lee, Sophia Yu, Mary K. Durbin, Aditya Nair, Rishi P. Singh, Katherine E. Talcott, Alline G. Melo, et al. "Student Becomes Teacher: Training Faster Deep Learning Lightweight Networks for Automated Identification of Optical Coherence Tomography B-Scans of Interest Using a Student-Teacher Framework." *Biomedical Optics Express*, 12(9):5387–5399, 2021.

[174] Megha J. Padghamod and Jayanand P. Gawande. "Classification of Ultrasonic Uterine Images." *Advances in Research on Electrical and Electronic Engineering*, 1(3):89–92, 2014.

[175] Chao Peng, Xiangyu Zhang, Gang Yu, Guiming Luo, and Jian Sun. "Large Kernel Matters – Improve Semantic Segmentation by Global Convolutional Network." In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 4353–4361, 2017.

[176] Emmanuel Pintelas, Ioannis E. Livieris, Nikolaos Barotsis, George Panayiotakis, and Panagiotis Pintelas. "An Autoencoder Convolutional Neural Network Framework for Sarcopenia Detection Based on Multi-Frame Ultrasound Image Slices." In *IFIP International Conference on Artificial Intelligence Applications and Innovations*, pages 209–219. Springer, 2021.

[177] Tobias Pohlen, Alexander Hermans, Markus Mathias, and Bastian Leibe. "Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes." In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 4151–4160, 2017.

[178] Xiaofeng Qi, Lei Zhang, Yao Chen, Yong Pi, Yi Chen, Qing Lv, and Zhang Yi. "Automated Diagnosis of Breast Ultrasonography Images Using Deep Neural Networks." *Medical Image Analysis*, 52:185–198, 2019.

[179] Dian Qin, Jia-Jun Bu, Zhe Liu, Xin Shen, Sheng Zhou, Jing-Jun Gu, Zhi-Hua Wang, Lei Wu, and Hui-Fen Dai. "Efficient Medical Image Segmentation Based on Knowledge Distillation." *IEEE Transactions on Medical Imaging*, 40(12):3820–3831, 2021.

[180] Xiaolei Qu, Yao Shi, Yaxin Hou, and Jue Jiang. "An Attention-Supervised Full-Resolution Residual Network for the Segmentation of Breast Ultrasound Images." *Medical Physics*, 47(11):5702–5714, 2020.

[181] Xiaolei Qu, Hongyan Lu, Wenzhong Tang, Shuai Wang, Dezhi Zheng, Yaxin Hou, and Jue Jiang. "A VGG Attention Vision Transformer Network for Benign and Malignant Classification of Breast Ultrasound Images." *Medical Physics*, 49(9):5787–5798, 2022.

[182] Reza Moradi Rad, Parvaneh Saeedi, Jason Au, and Jon Havelock. "Trophectoderm Segmentation in Human Embryo Images via Inceptioned U-Net." *Medical Image Analysis*, page 101612, 2020.

[183] A. Radford. "Improving Language Understanding by Generative Pre-Training." 2018.

[184] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. "Learning Transferable Visual Models from Natural Language Supervision." In *International Conference on Machine Learning*, pages 8748–8763. PMLR, 2021.

[185] Ingerid Reinertsen, D. Louis Collins, and Simon Drouin. "The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation." *Frontiers in Oncology*, 10:3219, 2021.

[186] Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. "FitNets: Hints for Thin Deep Nets." *arXiv Preprint arXiv:1412.6550*, 2014.

[187] O. Ronneberger, P. Fischer, and T. Brox. "U-Net: Convolutional Networks for Biomedical Image Segmentation." In *International Conference on Medical Image Computing and Computer-Assisted Intervention*, pages 234–241. Springer, 2015.

[188] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. "U-Net: Convolutional Networks for Biomedical Image Segmentation." In *Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18*, pages 234–241. Springer, 2015.

[189] J. M. Rubin, M. Mirfakhraee, E. E. Duda, G. J. Dohrmann, and F. Brown. "Intraoperative Ultrasound Examination of the Brain." *Radiology*, 137(3):831–832, 1980.

[190] Sebastian Ruder. "An Overview of Multi-Task Learning in Deep Neural Networks." *arXiv Preprint arXiv:1706.05098*, 2017.

[191] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. "ImageNet Large Scale Visual Recognition Challenge." *International Journal of Computer Vision (IJCV)*, 115(3):211–252, 2015. doi: 10.1007/s11263-015-0816-y.

[192] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. "MobileNetV2: Inverted Residuals and Linear Bottlenecks." In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 4510–4520, 2018.

[193] Rahul Sastry, Wenya Linda Bi, Steve Pieper, Sarah Frisken, Tina Kapur, William Wells III, and Alexandra J. Golby. "Applications of Ultrasound in the Resection of Brain Tumors." *Journal of Neuroimaging*, 27(1):5–15, 2017.

[194] Caroline A. Schneider, Wayne S. Rasband, and Kevin W. Eliceiri. "NIH Image to ImageJ: 25 Years of Image Analysis." *Nature Methods*, 9(7):671, 2012.

[195] Thomas Schneider, Christian Mawrin, Cordula Scherlach, Martin Skalej, and Raimund Firsching. "Gliomas in Adults." *Deutsches Ärzteblatt International*, 107(45):799, 2010.

[196] T. Selbekk, A. S. Jakola, O. Solheim, T. F. Johansen, F. Lindseth, I. Reinertsen, and G. Unsgard. "Ultrasound Imaging in Neurosurgery: Approaches to Minimize Surgically Induced Image Artefacts for Improved Resection Control." *Acta Neurochirurgica*, 155, 2013. doi: https://doi.org/10.1007/s00701-013-1647-7.

[197] Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. "Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization." In *Proceedings of the IEEE International Conference on Computer Vision*, pages 618–626, 2017.

[198] Bryar Shareef, Alex Vakanski, Min Xian, and Phoebe E. Freer. "ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation." *arXiv Preprint arXiv:2009.12894*, 2020.

[199] Bryar Shareef, Min Xian, and Aleksandar Vakanski. "STAN: Small Tumor-Aware Network for Breast Ultrasound Image Segmentation." In *2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)*, pages 1–5. IEEE, 2020.

[200] Mostafa Sharifzadeh, Habib Benali, and Hassan Rivaz. "Phase Aberration Correction: A Convolutional Neural Network Approach." *IEEE Access*, 8:162252–162260, 2020.

[201] Mostafa Sharifzadeh, Sobhan Goudarzi, An Tang, Habib Benali, and Hassan Rivaz. "Mitigating Aberration-Induced Noise: A Deep Learning-Based Aberration-to-Aberration Approach." *IEEE Transactions on Medical Imaging*, 2024.

[202] Dinggang Shen, Guorong Wu, and Heung-Il Suk. "Deep Learning in Medical Image Analysis." *Annual Review of Biomedical Engineering*, 19:221–248, 2017.

[203] Xiaoyan Shen, Liangyu Wang, Yu Zhao, Ruibo Liu, Wei Qian, and He Ma. "Dilated Transformer: Residual Axial Attention for Breast Ultrasound Image Segmentation." *Quantitative Imaging in Medicine and Surgery*, 12(9):4512, 2022.

[204] Jun Shi, Shichong Zhou, Xiao Liu, Qi Zhang, Minhua Lu, and Tianfu Wang. "Stacked Deep Polynomial Network Based Representation Learning for Tumor Classification with Small Ultrasound Image Dataset." *Neurocomputing*, 194:87–94, 2016.

[205] Seung Yeon Shin, Soochahn Lee, Il Dong Yun, Sun Mi Kim, and Kyoung Mu Lee. "Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images." *IEEE Transactions on Medical Imaging*, 38(3):762–774, 2018.

[206] Karen Simonyan and Andrew Zisserman. "Very Deep Convolutional Networks for Large-Scale Image Recognition." *arXiv Preprint arXiv:1409.1556*, 2014.

[207] Vivek Kumar Singh, Mohamed Abdel-Nasser, Farhan Akram, Hatem A. Rashwan, Md Mostafa Kamal Sarker, Nidhi Pandey, Santiago Romani, and Domenec Puig. "Breast Tumor Segmentation in Ultrasound Images Using Contextual-Information-Aware Deep Adversarial Learning Framework." *Expert Systems with Applications*, 162:113870, 2020.

[208] Leslie N. Smith. "Cyclical Learning Rates for Training Neural Networks." In *2017 IEEE Winter Conference on Applications of Computer Vision (WACV)*, pages 464–472. IEEE, 2017.

[209] Carlos Sobral, José Silvestre Silva, Alexandra André, and Jaime B. Santos. "Sarcopenia Diagnosis: Deep Transfer Learning Versus Traditional Machine Learning."

[210] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. "Deep Unsupervised Learning Using Nonequilibrium Thermodynamics." In *International Conference on Machine Learning*, pages 2256–2265. PMLR, 2015.

[211] Praotasna Sombune, Phongphan Phienphanich, Sutanya Phuechpanpaisal, Sombat Muengtaweepongsa, Anuchit Ruamthanthong, and Charturong Tantibundhit. "Automated Embolic Signal Detection Using Deep Convolutional Neural Network." In *2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)*, pages 3365–3368. IEEE, 2017.

[212] Hannah Spitzer, Kai Kiwitz, Katrin Amunts, Stefan Harmeling, and Timo Dickscheid. "Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-Supervised Siamese Networks." In *International Conference on Medical Image Computing and Computer-Assisted Intervention*, pages 663–671. Springer, 2018.

[213] Andrej Šteňo, Ján Buvala, Veronika Babková, Adrián Kiss, David Toma, and Alexander Lysak. "Current Limitations of Intraoperative Ultrasound in Brain Tumor Surgery." *Frontiers in Oncology*, 11:851, 2021.

[214] Matt S. Stock and Brennan J. Thompson. "Echo Intensity as an Indicator of Skeletal Muscle Quality: Applications, Methodology, and Future Directions." *European Journal of Applied Physiology*, 121:369–380, 2021.

[215] Howard J. Stringer and Daisy Wilson. "The Role of Ultrasound as a Diagnostic Tool for Sarcopenia." *The Journal of Frailty & Aging*, 7(4):258–261, 2018.

[216] Run Su, Deyun Zhang, Jinhuai Liu, and Chuandong Cheng. "MSU-Net: Multi-Scale U-Net for 2D Medical Image Segmentation." *Frontiers in Genetics*, 12:140, 2021.

[217] Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. "Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation." *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 42(6):1408–1423, 2019.

[218] Jiawei Sun, Bobo Wu, Tong Zhao, Liugang Gao, Kai Xie, Tao Lin, Jianfeng Sui, Xiaoqin Li, Xiaojin Wu, and Xinye Ni. "Classification for Thyroid Nodule Using ViT with Contrastive Learning in Ultrasound Images." *Computers in Biology and Medicine*, 152:106444, 2023.

[219] Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey N. Chiang, Zhihao Wu, and Xiaowei Ding. "Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation." *Medical Image Analysis*, 63:101693, 2020.

[220] Mingxing Tan and Quoc Le. "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks." In *International Conference on Machine Learning*, pages 6105–6114. PMLR, 2019.

[221] Ali K.Z. Tehrani, Mina Amiri, Ivan M. Rosado-Mendez, Timothy J. Hall, and Hassan Rivaz. "Ultrasound Scatterer Density Classification Using Convolutional Neural Networks and Patch Statistics." *IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control*, 2021.

[222] Klaus D. Toennies. *Guide to Medical Image Analysis*. Springer, 2017.

[223] Helena R. Torres, Pedro Morais, Bruno Oliveira, Cahit Birdir, Mario Rüdiger, Jaime C. Fonseca, and João L. Vilaca. "A Review of Image Processing Methods for Fetal Head and Brain Analysis in Ultrasound Images." *Computer Methods and Programs in Biomedicine*, 215:106629, 2022.

[224] Frederick Tung and Greg Mori. "Similarity-Preserving Knowledge Distillation." In *Proceedings of the IEEE/CVF International Conference on Computer Vision*, pages 1365–1374, 2019.

[225] Nishant Uniyal, Hani Eskandari, Purang Abolmaesumi, Samira Sojoudi, Paula Gordon, Linda Warren, Robert N. Rohling, Septimiu E. Salcudean, and Mehdi Moradi. "Ultrasound RF Time Series for Classification of Breast Lesions." *IEEE Transactions on Medical Imaging*, 34(2):652–661, 2014.

[226] Jesper E. Van Engelen and Holger H. Hoos. "A Survey on Semi-Supervised Learning." *Machine Learning*, 109(2):373–440, 2020.

[227] Vincent Vanhoucke, Andrew Senior, Mark Z. Mao, et al. "Improving the Speed of Neural Networks on CPUs." In *Proc. Deep Learning and Unsupervised Feature Learning NIPS Workshop*, volume 1, page 4, 2011.

[228] Sagar Vaze, Weidi Xie, and Ana I.L. Namburete. "Low-Memory CNNs Enabling Real-Time Ultrasound Segmentation Towards Mobile Deployment." *IEEE Journal of Biomedical and Health Informatics*, 24(4):1059–1069, 2020.

[229] Guotai Wang, Wenqi Li, Sébastien Ourselin, and Tom Vercauteren. "Automatic Brain Tumor Segmentation Using Convolutional Neural Networks with Test-Time Augmentation." In *International MICCAI Brainlesion Workshop*, pages 61–72. Springer, 2018.

[230] Heng Wang, Donghao Zhang, Yang Song, Siqi Liu, Yue Wang, Dagan Feng, Hanchuan Peng, and Weidong Cai. "Segmenting Neuronal Structure in 3D Optical Microscope Images via Knowledge Distillation with Teacher-Student Network." In *2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)*, pages 228–231. IEEE, 2019.

[231] Lin Wang and Kuk-Jin Yoon. "Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks." *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 2021.

[232] Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu. "A Comprehensive Survey of Continual Learning: Theory, Method and Application." *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 2024.

[233] Yi Wang, Zijun Deng, Xiaowei Hu, Lei Zhu, Xin Yang, Xuemiao Xu, Pheng-Ann Heng, and Dong Ni. "Deep Attentional Features for Prostate Segmentation in Ultrasound." In *International Conference on Medical Image Computing and Computer-Assisted Intervention*, pages 523–530. Springer, 2018.

[234] Juerd Wijntjes and Nens van Alfen. "Muscle Ultrasound: Present State and Future Opportunities." *Muscle & Nerve*, 63(4):455–466, 2021.

[235] Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng. "Quantized Convolutional Neural Networks for Mobile Devices." In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 4820–4828, 2016.

[236] Kaizhi Wu, Xi Chen, and Mingyue Ding. "Deep Learning Based Classification of Focal Liver Lesions with Contrast-Enhanced Ultrasound." *Optik*, 125(15):4057–4063, 2014.

[237] C. Xia, J. Li, X. Chen, A. Zheng, and Y. Zhang. "What Is and What Is Not a Salient Object? Learning Salient Object Detector by Ensembling Linear Exemplar Regressors." In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 4142–4150, 2017.

[238] Menghua Xia, Hongbo Yang, Yanan Qu, Yi Guo, Guohui Zhou, Feng Zhang, and Yuanyuan Wang. "Multilevel Structure-Preserved GAN for Domain Adaptation in Intravascular Ultrasound Analysis." *Medical Image Analysis*, 82:102614, 2022.

[239] Min Xian, Yingtao Zhang, Heng-Da Cheng, Fei Xu, Boyu Zhang, and Jianrui Ding. "Automatic Breast Ultrasound Image Segmentation: A Survey." *Pattern Recognition*, 79:340–355, 2018.

[240] Yiming Xiao, Maryse Fortin, Geirmund Unsgård, Hassan Rivaz, and Ingerid Reinertsen. "Retrospective Evaluation of Cerebral Tumors (RESECT): A Clinical Database of Pre-operative MRI and Intra-operative Ultrasound in Low-Grade Glioma Surgeries." *Medical Physics*, 44(7):3875–3882, 2017.

[241] Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. "Aggregated Residual Transformations for Deep Neural Networks." In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 1492–1500, 2017.

[242] Bing Xu, Naiyan Wang, Tianqi Chen, and Mu Li. "Empirical Evaluation of Rectified Activations in Convolutional Networks." *arXiv Preprint* arXiv:1505.00853, 2015.

[243] Chunbo Xu, Yunliang Qi, Yiming Wang, Meng Lou, Jiande Pi, and Yide Ma. "ARF-Net: An Adaptive Receptive Field Network for Breast Mass Segmentation in Whole Mammograms and Ultrasound Images." *Biomedical Signal Processing and Control*, 71:103178, 2022.

[244] Kunran Xu, Lai Rui, Yishi Li, and Lin Gu. "Feature Normalized Knowledge Distillation for Image Classification." In *European Conference on Computer Vision*, pages 664–680. Springer, 2020.

[245] Meng Xu, Kuan Huang, Qiuxiao Chen, and Xiaojun Qi. "MSSA-Net: Multi-Scale Self-Attention Network for Breast Ultrasound Image Segmentation." In *2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)*, pages 827–831. IEEE, 2021.

[246] Yuan Xu, Yuxin Wang, Jie Yuan, Qian Cheng, Xueding Wang, and Paul L. Carson. "Medical Breast Ultrasound Image Segmentation by Machine Learning." *Ultrasonics*, 91:1–9, 2019.

[247] Jian Yang, Haoyang Cai, Zhi-Xiong Xiao, Hangyu Wang, and Ping Yang. "Effect of Radiotherapy on the Survival of Cervical Cancer Patients: An Analysis Based on SEER Database." *Medicine*, 98(30), 2019.

[248] Kaiwen Yang, Aiga Suzuki, Jiaxing Ye, Hirokazu Nosato, Ayumi Izumori, and Hidenori Sakanashi. "CTG-Net: Cross-Task Guided Network for Breast Ultrasound Diagnosis." *PloS One*, 17(8):e0271106, 2022.

[249] Min-Chun Yang, Woo Kyung Moon, Yu-Chiang Frank Wang, Min Sun Bae, Chiun-Sheng Huang, Jeon-Hor Chen, and Ruey-Feng Chang. "Robust Texture Analysis Using Multi-Resolution Gray-Scale Invariant Features for Breast Sonographic Tumor Diagnosis." *IEEE Transactions on Medical Imaging*, 32(12):2262–2273, 2013.

[250] Moi Hoon Yap, Gerard Pons, Joan Martí, Sergi Ganau, Melcior Sentís, Reyer Zwiggelaar, Adrian K. Davison, and Robert Martí. "Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks." *IEEE Journal of Biomedical and Health Informatics*, 22(4):1218–1226, 2017.

[251] Moi Hoon Yap, Manu Goyal, Fatima Osman, Ezak Ahmad, Robert Martí, Erika Denton, Arne Juette, and Reyer Zwiggelaar. "End-to-End Breast Ultrasound Lesions Recognition with a Deep Learning Approach." In *Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging*, volume 10578, page 1057819. International Society for Optics and Photonics, 2018.

[252] Ujwal Yeole, Vikas Singh, Ajit Mishra, Salman Shaikh, Prakash Shetty, and Aliasgar Moiyadi. "Navigated Intraoperative Ultrasonography for Brain Tumors: A Pictorial Essay on the Technique, Its Utility, and Its Benefits in Neuro-Oncology." *Ultrasonography*, 39(4):394, 2020.

[253] Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb, and Leonardo Rundo. "Unified Focal Loss: Generalising Dice and Cross Entropy-Based Losses to Handle Class Imbalanced Medical Image Segmentation." *Computerized Medical Imaging and Graphics*, 95:102026, 2022.

[254] Yongjian Yu and Scott T. Acton. "Edge Detection in Ultrasound Imagery Using the Instantaneous Coefficient of Variation." *IEEE Transactions on Image Processing*, 13(12):1640–1655, 2004.

[255] Paul A. Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Gee, and Guido Gerig. "User-Guided 3D Active Contour Segmentation of Anatomical Structures: Significantly Improved Efficiency and Reliability." *NeuroImage*, 31(3):1116–1128, 2006.

[256] Sergey Zagoruyko and Nikos Komodakis. "Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer." *arXiv preprint* arXiv:1612.03928, 2016.

[257] Jiansong Zhang, Yongjian Chen, and Peizhong Liu. "Automatic Recognition of Standard Liver Sections Based on Vision-Transformer." In *2022 IEEE 16th International Conference on Anti-Counterfeiting, Security, and Identification (ASID)*, pages 1–4. IEEE, 2022.

[258] Jiashu Zhang, Xiaolei Chen, Yan Zhao, Fei Wang, Fangye Li, and Bainan Xu. "Impact of Intraoperative Magnetic Resonance Imaging and Functional Neuronavigation on Surgical Outcome in Patients with Gliomas Involving Language Areas." *Neurosurgical Review*, 38(2):319–330, 2015.

[259] Yu Zhang, Yuanyuan Wang, Weiqi Wang, and Bin Liu. "Doppler Ultrasound Signal Denoising Based on Wavelet Frames." *IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control*, 48(3):709–716, 2001.

[260] Feng Zhao and Xianghua Xie. "An Overview of Interactive Medical Image Segmentation." *Annals of the BMVA*, 2013(7):1–22, 2013.

[261] Hang Zhou and Hassan Rivaz. "Registration of Pre-and Postresection Ultrasound Volumes with Noncorresponding Regions in Neurosurgery." *IEEE Journal of Biomedical and Health Informatics*, 20(5):1240–1249, 2016.

[262] Kevin Zhou. *Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches.* Academic Press, 2015.

[263] Lichen Zhou, Chuang Zhang, and Ming Wu. "D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction." In *CVPR Workshops*, pages 182–186, 2018.

[264] Qikui Zhu, Bo Du, Baris Turkbey, Peter L. Choyke, and Pingkun Yan. "Deeply-Supervised CNN for Prostate Segmentation." In *2017 International Joint Conference on Neural Networks (IJCNN)*, pages 178–184. IEEE, 2017.

[265] Zhemin Zhuang, Nan Li, Alex Noel Joseph Raj, Vijayalakshmi GV Mahesh, and Shunmin Qiu. "An RDau-Net Model for Lesion Segmentation in Breast Ultrasound Images." *PloS One*, 14(8):e0221535, 2019.

[266] Laurent Zieleskiewicz, Laurent Muller, Karim Lakhal, Zoe Meresse, Charlotte Arbelot, Pierre-Marie Bertrand, Belaid Bouhemad, Bernard Cholley, Didier Demory, Serge Duperret, et al. "Point-of-Care Ultrasound in Intensive Care Units: Assessment of 1073 Procedures in a Multicentric, Prospective, Observational Study." *Intensive Care Medicine*, 41(9):1638–1647, 2015.

[267] Jesse Zuckerman, Matthew Ades, Louis Mullie, Amanda Trnkus, Jean-Francois Morin, Yves Langlois, Felix Ma, Mark Levental, José A. Morais, and Jonathan Afilalo. "Psoas Muscle Area and Length of Stay in Older Adults Undergoing Cardiac Operations." *The Annals of Thoracic Surgery*, 103(5):1498–1504, 2017.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top