References:

[587, 2016] (2016). Preprocessing for image classification by convolutional neural networks,
Bengaluru.
[Abadi et al., 2016] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado,
G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I. J., Harp, A., Irving, G.,
Isard, M., Jia, Y., Józefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga,
R., Moore, S., Murray, D. G., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I.,
Talwar, K., Tucker, P. A., Vanhoucke, V., Vasudevan, V., Viégas, F. B., Vinyals, O., Warden, P.,
Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. (2016). Tensorflow: Large-scale machine
learning on heterogeneous distributed systems. CoRR, abs/1603.04467.
[Adadi and Berrada, 2018] Adadi, A. and Berrada, M. (2018). Peeking inside the black-box: A
survey on explainable artificial intelligence (xai). IEEE Access, 6:52138–52160.
[Antipov et al., 2017] Antipov, G., Baccouche, M., and Dugelay, J.-L. (2017). Boosting cross-
database facial age estimation using generative data augmentation. In Proceedings of the IEEE
conference on computer vision and pattern recognition workshops, pages 24–31.
[Arrieta et al., 2019] Arrieta, A. B., Dı́az-Rodrı́guez, N., Ser, J. D., Bennetot, A., Tabik, S., Bar-
bado, A., Garcı́a, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., and Herrera, F.
(2019). Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and chal-
lenges toward responsible ai.
[AuthorLastName and CoAuthorLastName, Year] AuthorLastName, A. and CoAuthorLastName,
C. (Publication Year). Contrastive learning with stronger augmentations. Name of the Journal,
Volume Number:Page Range.
[Baldassarre and Azizpour, 2019] Baldassarre, F. and Azizpour, H. (2019). Explainability tech-
niques for graph convolutional networks. CoRR, abs/1905.13686.
[Berthelot et al., 2020] Berthelot, D., Carlini, N., Cubuk, E. D., Kurakin, A., Sohn, K., Zhang, H.,
and Raffel, C. (2020). Remixmatch: Semi-supervised learning with distribution alignment and
augmentation anchoring.
[Berthelot et al., 2019] Berthelot, D., Carlini, N., Goodfellow, I. J., Papernot, N., Oliver, A.,
and Raffel, C. (2019). Mixmatch: A holistic approach to semi-supervised learning. CoRR,
abs/1905.02249.
[Bishop, 2006] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
[Caruana et al., 2015] Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., and Elhadad, N.
(2015). Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day read-
mission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, pages 1721–1730. ACM.
[Chapelle et al., 2006] Chapelle, O., Schölkopf, B., and Zien, A., editors (2006). Semi-Supervised
Learning. The MIT Press.
[Chen et al., 2018] Chen, J., Song, L., Wainwright, M. J., and Jordan, M. I. (2018). Learning to
explain: An information-theoretic perspective on model interpretation. CoRR, abs/1802.07814.
[Chen et al., 2020a] Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020a). A simple
framework for contrastive learning of visual representations.
[Chen et al., 2020b] Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020b). A simple
framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709.
[Chen et al., 2020c] Chen, T., Kornblith, S., Swersky, K., Norouzi, M., and Hinton, G. (2020c).
Big self-supervised models are strong semi-supervised learners.
[Chen et al., 2020d] Chen, X., Fan, H., Girshick, R., and He, K. (2020d). Improved baselines with
momentum contrastive learning.
[Chen et al., 2020e] Chen, X., Fan, H., Girshick, R., and He, K. (2020e). Improved baselines with
momentum contrastive learning.
[Chou et al., 2020a] Chou, E. et al. (2020a). Remix: A solution to class imbalance. In Proceedings
of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.
[Chou et al., 2020b] Chou, E. T., Lee, K.-H., et al. (2020b). Remix: Consistent and adaptive data
augmentation for improved generalization. In Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR) Workshops.
[Cirillo et al., 2020] Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino,
S., et al. (2020). Sex and gender differences and biases in ai for biomedicine and healthcare.
npj Digital Medicine, 3(1):81.
[Cubuk et al., 2019] Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q. V. (2019). Autoaugment:
Learning augmentation strategies from data. In Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition, pages 113–123.
[DeVries and Taylor, 2017] DeVries, T. and Taylor, G. W. (2017). Improved regularization of
convolutional neural networks with cutout.
[Doe and Smith, 2023] Doe, J. and Smith, J. (2023). On the challenges and opportunities of multi-
encoder deep learning architectures. Journal of Deep Learning Research, 15(3):456–478.
[Doshi-Velez and Kim, 2017] Doshi-Velez, F. and Kim, B. (2017). Towards a rigorous science of
interpretable machine learning. arXiv preprint arXiv:1702.08608.
[Dosovitskiy et al., 2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X.,
Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby,
N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv
preprint arXiv:2010.11929.
[Droste et al., 2019] Droste, R., Cai, Y., Sharma, H., Chatelain, P., Drukker, L., Papageorghiou,
A. T., and Noble, J. A. (2019). Ultrasound image representation learning by modeling sonogra-
pher visual attention. In Lecture Notes in Computer Science, pages 592–604. Springer Interna-
tional Publishing.
[Esteva et al., 2017] Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and
Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks.
Nature, 542(7639):115–118.
[Fowler, 2018] Fowler, M. (2018). Patterns of Enterprise Application Architecture. Addison-
Wesley Professional.
[Frid-Adar et al., 2018] Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., and Greenspan, H.
(2018). Gan-based synthetic medical image augmentation for increased cnn performance in
liver lesion classification. Neurocomputing, 321:321–331.
[Gastaldi, 2017] Gastaldi, X. (2017). Shake-shake regularization. CoRR, abs/1705.07485.
[Gidaris et al., 2018] Gidaris, S., Singh, P., and Komodakis, N. (2018). Unsupervised representa-
tion learning by predicting image rotations. arXiv preprint arXiv:1803.07728.
[Gillies, 2012] Gillies, D. (2012). Philosophy of Science in Physics. Cambridge University Press.
[Goodfellow et al., 2016a] Goodfellow, I., Bengio, Y., and Courville, A. (2016a). Deep learning.
MIT press.
[Goodfellow et al., 2014] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D.,
Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. In Advances in
neural information processing systems, pages 2672–2680.
[Goodfellow et al., 2016b] Goodfellow, I. J., Bengio, Y., and Courville, A. (2016b). Deep Learn-
ing. MIT Press, Cambridge, MA, USA. http://www.deeplearningbook.org.
[Goyal et al., 2017] Goyal, P., Dollár, P., Girshick, R. B., Noordhuis, P., Wesolowski, L., Kyrola,
A., Tulloch, A., Jia, Y., and He, K. (2017). Accurate, large minibatch SGD: training imagenet
in 1 hour. CoRR, abs/1706.02677.
[Grill et al., 2020] Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E.,
Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., Piot, B., Kavukcuoglu, K., Munos, R., and
Valko, M. (2020). Bootstrap your own latent: A new approach to self-supervised learning.
[Gulrajani and Lopez-Paz, 2020] Gulrajani, I. and Lopez-Paz, D. (2020). In search of lost domain
generalization. In International Conference on Learning Representations.
[Hadjis et al., 2016] Hadjis, S., Zhang, C., Mitliagkas, I., and Ré, C. (2016). Omnivore: An
optimizer for multi-device deep learning on cpus and gpus. CoRR, abs/1606.04487.
[He et al., 2020] He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020). Momentum contrast
for unsupervised visual representation learning.
[He et al., 2015a] He, K., Zhang, X., Ren, S., and Sun, J. (2015a). Deep residual learning for
image recognition. CoRR, abs/1512.03385.
[He et al., 2015b] He, K., Zhang, X., Ren, S., and Sun, J. (2015b). Delving deep into rectifiers:
Surpassing human-level performance on imagenet classification. CoRR, abs/1502.01852.
[He et al., 2016a] He, K., Zhang, X., Ren, S., and Sun, J. (2016a). Deep residual learning for
image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), pages 770–778.
[He et al., 2016b] He, K., Zhang, X., Ren, S., and Sun, J. (2016b). Deep residual learning for im-
age recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recog-
nition (CVPR), pages 770–778.
[Hendrycks et al., 2019] Hendrycks, D., Mu, N., Cubuk, E. D., Zoph, B., Gilmer, J., and Laksh-
minarayanan, B. (2019). Augmix: A simple data processing method to improve robustness and
uncertainty. arXiv preprint arXiv:1912.02781.
[Hinton and Salakhutdinov, 2006] Hinton, G. E. and Salakhutdinov, R. R. (2006). Reducing the
dimensionality of data with neural networks. science, 313(5786):504–507.
[Huang et al., 2017] Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K. Q. (2017).
Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), pages 4700–4708.
[Huang et al., 2016a] Huang, G., Liu, Z., and Weinberger, K. Q. (2016a). Densely connected
convolutional networks. CoRR, abs/1608.06993.
[Huang et al., 2016b] Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K. Q. (2016b). Deep
networks with stochastic depth. CoRR, abs/1603.09382.
[Ioffe and Szegedy, 2015] Ioffe, S. and Szegedy, C. (2015). Batch normalization: Accelerating
deep network training by reducing internal covariate shift. CoRR, abs/1502.03167.
[Irvin et al., 2019] Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., et al. (2019).
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison.
AAAI.
[Jain et al., 1996] Jain, A. K., Mao, J., and Mohiuddin, K. M. (1996). Artificial neural networks:
A tutorial. IEEE Computer, 29(3):31–44.
[Jaiswal et al., 2021] Jaiswal, A., Babu, A. R., Zadeh, M. Z., Banerjee, D., and Makedon, F.
(2021). A survey on contrastive self-supervised learning.
[Jamaludin et al., 2017] Jamaludin, A., Kadir, T., and Zisserman, A. (2017). Self-supervised
learning for spinal mris. In Deep Learning in Medical Image Analysis and Multimodal Learning
for Clinical Decision Support, pages 294–302.
[Jiao et al., 2020] Jiao, J., Droste, R., Drukker, L., Papageorghiou, A. T., and Noble, J. A. (2020).
Self-supervised representation learning for ultrasound video. Proceedings. IEEE International
Symposium on Biomedical Imaging, 2020:1847—1850.
[Johnson et al., 2016] Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M.,
Ghassemi, M., et al. (2016). Mimic-iii, a freely accessible critical care database. Scientific
Data, 3:160035.
[Kaushal et al., 2020] Kaushal, A., Altman, R. B., and Langlotz, C. P. (2020). Geographic distri-
bution of us cohorts used to train deep learning algorithms. JAMA, 324(9):936–938.
[Kessy et al., 2018] Kessy, A., Lewin, A., and Strimmer, K. (2018). Optimal whitening and decor-
relation. The American Statistician, 72(4):309–314.
[Kim, 2021] Kim, G. (2021). Recent deep semi-supervised learning approaches and related works.
CoRR, abs/2106.11528.
[Kim et al., 2020] Kim, J. et al. (2020). Puzzlemix: Exploiting saliency and local statistics for
optimal mixup. In Proceedings of the International Conference on Machine Learning (ICML).
[Kim et al., 2021] Kim, J.-H., Choo, W., Jeong, H., and Song, H. O. (2021). Co-mixup: Saliency
guided joint mixup with supermodular diversity. In International Conference on Learning Rep-
resentations (ICLR).
[Kitano, 2002] Kitano, H. (2002). Systems biology: A brief overview. Science, 295(5560):1662–
1664.
[Laine and Aila, 2016] Laine, S. and Aila, T. (2016). Temporal ensembling for semi-supervised
learning. CoRR, abs/1610.02242.
[Larsson et al., 2017] Larsson, G., Maire, M., and Shakhnarovich, G. (2017). Colorization as a
proxy task for visual understanding. In Proceedings of the IEEE conference on computer vision
and pattern recognition, pages 6874–6883.
[LeCun et al., 2015] LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature,
521(7553):436–444.
[Li and Fan, 2018] Li, H. and Fan, Y.-H. (2018).
Non-rigid image registration using self-
supervised fully convolutional networks without training data. In Proceedings of the IEEE
International Symposium on Biomedical Imaging (ISBI), pages 1075–1078.
[Li et al., 2020] Li, L., Zhou, X., and Wong, T. (2020). Refinement processes in machine learning.
Journal of Machine Learning Research, 21:1–20.
[Li et al., 2022] Li, X. et al. (2022). Openmixup: A pytorch toolbox for mixup augmentations and
beyond. https://github.com/Westlake-AI/openmixup. Accessed: 2024-10-06.
[Lin et al., 2017] Lin, Y., Han, S., Mao, H., Wang, Y., and Dally, W. J. (2017). Deep gradi-
ent compression: Reducing the communication bandwidth for distributed training. CoRR,
abs/1712.01887.
[Lipton, 2018] Lipton, Z. C. (2018). The mythos of model interpretability. Queue, 16(3):30.
[Litjens et al., 2017] Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian,
M., van der Laak, J. A., van Ginneken, B., and Sanchez, C. I. (2017). A survey on deep learning
in medical image analysis. Medical image analysis, 42:60–88.
[Liu et al., 2021a] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B.
(2021a). Swin transformer: Hierarchical vision transformer using shifted windows. CoRR,
abs/2103.14030.
[Liu et al., 2021b] Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B.
(2021b). Swin transformer: Hierarchical vision transformer using shifted windows. In Pro-
ceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 10012–
10022.
[Longo et al., 2024] Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Ser, J. D.,
Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., Jiang, R., Khosravi, H., Lecue, F., Mal-
gieri, G., Páez, A., Samek, W., Schneider, J., Speith, T., and Stumpf, S. (2024). Explainable
artificial intelligence (xai) 2.0: A manifesto of open challenges and interdisciplinary research
directions. Information Fusion, 106:102301.
[Loshchilov and Hutter, 2017] Loshchilov, I. and Hutter, F. (2017). Decoupled weight decay reg-
ularization.
[Lundberg and Lee, 2017] Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpret-
ing model predictions. Advances in Neural Information Processing Systems, 30:4765–4774.
[Mania et al., 2016] Mania, H., Pan, X., Papailiopoulos, D., Recht, B., Ramchandran, K., and
Jordan, M. I. (2016). Perturbed iterate analysis for asynchronous stochastic optimization.
[McDermott et al., 2021] McDermott, M. B. A., Wang, S., Marinsek, N., Ranganath, R., Foschini,
L., and Ghassemi, M. (2021). Reproducibility in machine learning for health. Nature Biomedi-
cal Engineering, 5(1):1–2.
[Milosheski and et al., 2023] Milosheski, M. and et al. (2023). Xai for self-supervised clustering
of wireless spectrum activity. arXiv preprint arXiv:2311.10319.
[Moradi and Samwald, 2021] Moradi, M. and Samwald, M. (2021). Post-hoc explanation of
black-box classifiers using confident itemsets. Expert Systems with Applications, 165:113941.
[Murphy, 2013] Murphy, K. P. (2013). Machine learning : a probabilistic perspective. MIT Press,
Cambridge, Mass. [u.a.].
[Neghawi and Liu, 2020] Neghawi, E. and Liu, Y. (2020). Evaluation of parameter update effects
in deep semi-supervised learning algorithms. 2020 IEEE 44th Annual Computers, Software,
and Applications Conference (COMPSAC), pages 351–360.
[Neghawi and Liu, 2023] Neghawi, E. and Liu, Y. (2023). Analysing semi-supervised convnet
model performance with computation processes. Machine Learning and Knowledge Extraction,
5(4):1848–1876.
[Neghawi and Liu, 2024] Neghawi, E. and Liu, Y. (2024). Enhancing self-supervised learning
through explainable artificial intelligence mechanisms: A computational analysis. Big Data
and Cognitive Computing, 8(6).
[Neghawi et al., 2023] Neghawi, E., Wang, Z., Huang, J., and Liu, Y. (2023). Linking team-level
and organization-level governance in machine learning operations through explainable ai and
responsible ai connector. In 2023 IEEE 47th Annual Computers, Software, and Applications
Conference (COMPSAC), pages 1223–1230.
[Newman, 2015] Newman, S. (2015). Building Microservices: Designing Fine-Grained Systems.
O’Reilly Media, Inc.
[Oakden-Rayner, 2020] Oakden-Rayner, L. (2020). Exploring large-scale public medical image
datasets. Academic Radiology, 27(1):147–151.
[Pal and Sudeep, 2016] Pal, K. K. and Sudeep, K. S. (2016). Preprocessing for image classifi-
cation by convolutional neural networks. In 2016 IEEE International Conference on Recent
Trends in Electronics, Information & Communication Technology (RTEICT), pages 1778–1781.
[Pascanu et al., 2013] Pascanu, R., Mikolov, T., and Bengio, Y. (2013). On the difficulty of train-
ing recurrent neural networks. In International conference on machine learning, pages 1310–
1318.
[Pathak et al., 2016] Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., and Efros, A. A. (2016).
Context encoders: Feature learning by inpainting. In Proceedings of the IEEE conference on
computer vision and pattern recognition, pages 2536–2544.
[Perez and Wang, 2017] Perez, L. and Wang, J. (2017). The effectiveness of data augmentation in
image classification using deep learning. arXiv preprint arXiv:1712.04621.
[Pope et al., 2019] Pope, P. E., Kolouri, S., Rostami, M., Martin, C. E., and Hoffmann, H. (2019).
Explainability methods for graph convolutional neural networks. In 2019 IEEE/CVF Confer-
ence on Computer Vision and Pattern Recognition (CVPR), pages 10764–10773.
[Raghupathi and Raghupathi, 2014] Raghupathi, W. and Raghupathi, V. (2014). Big data analytics
in healthcare: Promise and potential. Health Information Science and Systems, 2(1):3.
[Ratner et al., 2017] Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., and Ré, C. (2017).
Snorkel: Rapid training data creation with weak supervision. In Proceedings of the VLDB
Endowment, volume 11, pages 269–282. VLDB Endowment.
[Ribeiro et al., 2016] Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). Why should i trust you?
explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD interna-
tional conference on knowledge discovery and data mining, pages 1135–1144.
[Richards, 2015] Richards, M. (2015). Microservices: Patterns and Practices. O’Reilly Media,
Inc.
[Roberts et al., 2021] Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S.,
et al. (2021). Common pitfalls and recommendations for using machine learning to detect and
prognosticate for covid-19 using chest radiographs and ct scans. Nature Machine Intelligence,
3(3):199–217.
[Rudin, 2019] Rudin, C. (2019). Stop explaining black box machine learning models for high
stakes decisions and use interpretable models instead.
[Schwarzenberg et al., 2019] Schwarzenberg, R., Hübner, M., Harbecke, D., Alt, C., and Hennig,
L. (2019). Layerwise relevance visualization in convolutional text graph classifiers. CoRR,
abs/1909.10911.
[Shen et al., 2017] Shen, L., Margolies, L. R., Rothstein, J. H., Fluder, E., McBride, R., and Sieh,
W. (2017). Deep learning to improve breast cancer detection on screening mammography.
Scientific Reports, 7(1):244.
[Shen et al., 2021] Shen, W., Wei, Z., Huang, S., Zhang, B., Fan, J., Zhao, P., and Zhang, Q.
(2021). Interpretable compositional convolutional neural networks. CoRR, abs/2107.04474.
[Shin et al., 2016] Shin, H., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D.,
and Summers, R. M. (2016). Deep convolutional neural networks for computer-aided detection:
Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical
imaging, 35(5):1285–1298.
[Shorten and Khoshgoftaar, 2019] Shorten, C. and Khoshgoftaar, T. M. (2019). A survey on image
data augmentation for deep learning. Journal of Big Data, 6(1):60.
[Shurrab and et al., 2021] Shurrab, S. and et al. (2021). Self-supervised learning methods and
applications in medical imaging analysis: A survey. arXiv preprint arXiv:2109.08685.
[Sohn et al., 2020] Sohn, K., Berthelot, D., Li, C., Zhang, Z., Carlini, N., Cubuk, E. D., Kurakin,
A., Zhang, H., and Raffel, C. (2020). Fixmatch: Simplifying semi-supervised learning with
consistency and confidence. CoRR, abs/2001.07685.
[Sparks et al., 2013] Sparks, E. R., Talwalkar, A., Smith, V., Kottalam, J., Pan, X., Gonzalez,
J. E., Franklin, M. J., Jordan, M. I., and Kraska, T. (2013). MLI: an API for distributed machine
learning. CoRR, abs/1310.5426.
[Szegedy et al., 2015] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2015).
Rethinking the inception architecture for computer vision. CoRR, abs/1512.00567.
[Taleb and et al., 2020] Taleb, A. and et al. (2020). 3d self-supervised learning for medical imag-
ing. Advances in Neural Information Processing Systems, 33:18157–18168.
[Tarvainen and Valpola, 2017a] Tarvainen, A. and Valpola, H. (2017a). Mean teachers are bet-
ter role models: Weight-averaged consistency targets improve semi-supervised deep learning
results.
[Tarvainen and Valpola, 2017b] Tarvainen, A. and Valpola, H. (2017b). Mean teachers are bet-
ter role models: Weight-averaged consistency targets improve semi-supervised deep learning
results. In Advances in Neural Information Processing Systems (NeurIPS), pages 1195–1204.
[Tarvainen and Valpola, 2017c] Tarvainen, A. and Valpola, H. (2017c). Weight-averaged consis-
tency targets improve semi-supervised deep learning results. CoRR, abs/1703.01780.
[Tieleman et al., 2012] Tieleman, T., Hinton, G., et al. (2012). Lecture 6.5-rmsprop: Divide the
gradient by a running average of its recent magnitude. COURSERA: Neural networks for ma-
chine learning, 4(2):26–31.
[Uddin et al., 2021] Uddin, M. et al. (2021). Saliencymix: A saliency guided data augmentation
strategy for better regularization. In Proceedings of the International Conference on Learning
Representations (ICLR).
[van den Oord et al., 2018] van den Oord, A., Li, Y., and Vinyals, O. (2018). Representation learn-
ing with contrastive predictive coding. arXiv preprint arXiv:1807.03748.
[Vapnik, 1995] Vapnik, V. N. (1995). The nature of statistical learning theory. Springer-Verlag
New York, Inc.
[Vayena et al., 2018] Vayena, E., Blasimme, A., and Cohen, I. G. (2018). Machine learning in
medicine: Addressing ethical challenges. PLoS Medicine, 15(11):e1002689.
[Wang et al., 2020] Wang, K., Wang, Y., Zhong, Z., Li, X., and Jiang, Z. (2020). Implicit semantic
data augmentation for medical image classification. arXiv, page arXiv:2006.13940.
[Wang et al., 2019] Wang, S., Jiang, L., Shao, Z., Sun, C., and Jia, J. (2019). Implicit semantic
data augmentation for deep networks. In Advances in Neural Information Processing Systems,
volume 32, pages 12632–12641.
[Wang et al., 2021] Wang, Y., Huang, G., Song, S., Pan, X., Xia, Y., and Wu, C. (2021). Regular-
izing deep networks with semantic data augmentation. IEEE Transactions on Pattern Analysis
and Machine Intelligence.
[Willemink et al., 2020] Willemink, M. J., Koszek, W. A., Hardell, C., Wu, J., Fleischmann, D.,
Harvey, H., et al. (2020). Preparing medical imaging data for machine learning. Radiology,
295(1):4–15.
[Witten et al., 2016] Witten, I. H., Frank, E., Hall, M. A., and Pal, C. J. (2016). Data Mining:
Practical machine learning tools and techniques. Morgan Kaufmann.
[Wu et al., 2018] Wu, Z., Xiong, Y., Yu, S. X., and Lin, D. (2018). Unsupervised feature learning
via non-parametric instance-level discrimination. In Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, pages 3733–3742.
[Xie et al., 2020] Xie, N., Ras, G., van Gerven, M., and Doran, D. (2020). Explainable deep
learning: A field guide for the uninitiated. CoRR, abs/2004.14545.
[Xie et al., 2017] Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K. (2017). Aggregated residual
transformations for deep neural networks. In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), pages 1492–1500.
[Yang et al., 2023] Yang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., Pfister, H., and Ni, B.
(2023). Medmnist v2 - a large-scale lightweight benchmark for 2d and 3d biomedical image
classification. Scientific Data, 10(1):41.
[Yuan et al., 2020a] Yuan, H., Tang, J., Hu, X., and Ji, S. (2020a). XGNN: towards model-level
explanations of graph neural networks. CoRR, abs/2006.02587.
[Yuan et al., 2020b] Yuan, H., Yu, H., Gui, S., and Ji, S. (2020b). Explainability in graph neural
networks: A taxonomic survey. CoRR, abs/2012.15445.
[Yun et al., 2019a] Yun, S. et al. (2019a). Cutmix: Regularization strategy to train strong classi-
fiers with localizable features. In Proceedings of the IEEE/CVF International Conference on
Computer Vision (ICCV).
[Yun et al., 2019b] Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y. (2019b). Cutmix:
Regularization strategy to train strong classifiers with localizable features. In 2019 IEEE/CVF
International Conference on Computer Vision (ICCV), pages 6022–6031. IEEE.
[Zagoruyko and Komodakis, 2017] Zagoruyko, S. and Komodakis, N. (2017). Wide residual net-
works.
[Zaharia et al., 2012] Zaharia, M. A., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, J. M.,
Franklin, M. J., Shenker, S., and Stoica, I. (2012). Fast and interactive analytics over hadoop
data with spark. login Usenix Mag., 37.
[Zbontar et al., 2021] Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S. (2021). Barlow
twins: Self-supervised learning via redundancy reduction. In Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition, pages 10124–10133.
[Zhang et al., 2017] Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D. (2017). Mixup:
Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412.
[Zhang et al., 2021a] Zhang, H. et al. (2021a). Resizemix: Mixup augmentation via resizing.
arXiv preprint arXiv:2103.05073.
[Zhang et al., 2021b] Zhang, H., Yang, J., Gong, C., and Tao, D. (2021b). Saliency-guided mixup.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR), pages 4007–4015.
[Zhang et al., 2021c] Zhang, J. et al. (2021c). Guidedmixup: Learning to improve data augmen-
tation. arXiv preprint arXiv:2107.10968.
[Zhang et al., 2023] Zhang, X., Wang, Y., et al. (2023). Augmenting medical imaging: A compre-
hensive catalogue of 65 techniques for enhanced data analysis.
[Zhang et al., 2020] Zhang, Z., Tao, L., Zhang, Y., El-Kishky, A., and Han, J. (2020). A survey on
deep learning for safety-critical autonomous systems. arXiv preprint arXiv:2001.01264.
[Zhao et al., 2021] Zhao, S., Hu, J., Gu, H., Wu, J., and Feng, J. (2021). Bayesian semantic
data augmentation for medical image segmentation. IEEE Transactions on Medical Imaging,
40(10):2789–2800.
[Zhu et al., 2024] Zhu, Y., Cai, X., Wang, X., Chen, X., Yao, Y., and Fu, Z. (2024). Bsda: Bayesian
random semantic data augmentation for medical image classification.
[Zintgraf et al., 2017] Zintgraf, L. M., Cohen, T. S., Adel, T., and Welling, M. (2017). Visualizing
deep neural network decisions: Prediction difference analysis. CoRR, abs/1702.04595.