[1] https://www.tfri.ca/about-cancer/cancer-types/cancer-type/brain [2] https://www.mayfieldclinic.com/pe-anatbrain.htm [3] https://www.newscientist.com/article/dn9969-introduction-the-human-brain/ [4] https://www.hopkinsmedicine.org/health/conditions-and-diseases/brain-tumor [5] Menze, B. H. et al. (2014). The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging, 34(10), 1993-2024. [6] Gibbs, P., Buckley, D. L., Blackband, S. J., & Horsman, A. (1996). Tumour volume determination from MR images by morphological segmentation. Physics in Medicine & Biology, 41(11), 2437. [7] Praveen, G. B., & Agrawal, A. (2015, November). Hybrid approach for brain tumor detection and classification in magnetic resonance images. In 2015 Communication, Control and Intelligent Systems (CCIS) (pp. 162-166). IEEE. [8] LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. [9] Matsugu, M., Mori, K., Mitari, Y., & Kaneda, Y. (2003). Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Networks, 16(5-6), 555-559. [10] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. [11] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). [12] https://medium.com/unpackai/batch-normalization-25905f889723. [13] https://en.wikipedia.org/wiki/Bilinear_interpolation. [14] Ng, D., & Feng, M. (2020). Medical Image Recognition: An Explanation and Hands-On Example of Convolutional Networks. In Leveraging Data Science for Global Health (pp. 263-284). Springer, Cham. [15] Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham. [16] Li, X., Chen, H., Qi, X., Dou, Q., Fu, C. W., & Heng, P. A. (2018). H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE transactions on medical imaging, 37(12), 2663-2674. [17] Salehi, S. S. M., Erdogmus, D., & Gholipour, A. (2017). Auto-context convolutional neural network (auto-net) for brain extraction in magnetic resonance imaging. IEEE transactions on medical imaging, 36(11), 2319-2330. [18] Rosas González, S., Birgui Sekou, T., Hidane, M., Zemmoura, I., & Tauber, C. (2021). Asymmetric Ensemble of Asymmetric U-Net Models for Brain Tumor Segmentation With Uncertainty Estimation. Frontiers in Neurology, 1421. [19] Luo, Z., Jia, Z., Yuan, Z., & Peng, J. (2020). Hdc-net: Hierarchical decoupled convolution network for brain tumor segmentation. IEEE Journal of Biomedical and Health Informatics, 25(3), 737-745. [20] Maji, D., Sigedar, P., & Singh, M. (2022). Attention Res-UNet with Guided Decoder for semantic segmentation of brain tumors. Biomedical Signal Processing and Control, 71, 103077. [21] Zhou, T., Ruan, S., Vera, P., & Canu, S. (2021). A Tri-Attention fusion guided multi-modal segmentation network. Pattern Recognition, 108417. [22] Chen, Y., Cao, Z., Cao, C., Yang, J., & Zhang, J. (2018, June). A modified U-Net for brain Mr image segmentation. In International Conference on Cloud Computing and Security (pp. 233-242). Springer, Cham. [23] Cahall, D. E., Rasool, G., Bouaynaya, N. C., & Fathallah-Shaykh, H. M. (2019). Inception modules enhance brain tumor segmentation. Frontiers in computational neuroscience, 13, 44. [24] Akil, M., Saouli, R., & Kachouri, R. (2020). Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy. Medical image analysis, 63, 101692. [25] Yogananda, C. G. B., Shah, B. R., Vejdani-Jahromi, M., Nalawade, S. S., Murugesan, G. K., Yu, F. F., ... & Maldjian, J. A. (2020). A fully automated deep learning network for brain tumor segmentation. Tomography, 6(2), 186-193. [26] Wang, L., Wang, S., Chen, R., Qu, X., Chen, Y., Huang, S., & Liu, C. (2019). Nested dilation networks for brain tumor segmentation based on magnetic resonance imaging. Frontiers in Neuroscience, 13, 285. [27] Zhang, J., Jiang, Z., Dong, J., Hou, Y., & Liu, B. (2020). Attention gate resU-Net for automatic MRI brain tumor segmentation. IEEE Access, 8, 58533-58545. [28] Huang, Z., Zhao, Y., Liu, Y., & Song, G. (2021). GCAUNet: A group cross-channel attention residual UNet for slice based brain tumor segmentation. Biomedical Signal Processing and Control, 70, 102958. [29] Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2019). Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging, 39(6), 1856-1867. [30] Zhou, T., Canu, S., Vera, P., & Ruan, S. (2021). Latent correlation representation learning for brain tumor segmentation with missing MRI modalities. IEEE Transactions on Image Processing, 30, 4263-4274. [31] Wang, B., Yang, J., Peng, H., Ai, J., An, L., Yang, B., ... & Ma, L. (2021). Brain Tumor Segmentation via Multi-Modalities Interactive Feature Learning. Frontiers in Medicine, 8. [32] Wang, G., Li, W., Ourselin, S., & Vercauteren, T. (2019). Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation. Frontiers in computational neuroscience, 13, 56. [33] Jiang, Z., Ding, C., Liu, M., & Tao, D. (2019, October). Two-stage cascaded U-Net: 1st place solution to BraTS challenge 2019 segmentation task. In International MICCAI brainlesion workshop (pp. 231-241). Springer, Cham. [34] Zhou, C., Ding, C., Wang, X., Lu, Z., & Tao, D. (2020). One-pass multi-task networks with cross-task guided attention for brain tumor segmentation. IEEE Transactions on Image Processing, 29, 4516-4529. [35] Li, X., Luo, G., & Wang, K. (2019, October). Multi-step cascaded networks for brain tumor segmentation. In International MICCAI Brainlesion Workshop (pp. 163-173). Springer, Cham. [36] https://www.med.upenn.edu/sbia/brats2018/evaluation.html [37] https://www.med.upenn.edu/cbica/brats2019/evaluation.html\ [38] Salehi, S. S. M., Erdogmus, D., & Gholipour, A. (2017, September). Tversky loss function for image segmentation using 3D fully convolutional deep networks. In International workshop on machine learning in medical imaging (pp. 379-387). Springer, Cham. [39] Huang, H., Yang, G., Zhang, W., Xu, X., Yang, W., Jiang, W., & Lai, X. (2021). A deep multi-task learning framework for brain tumor segmentation. Frontiers in Oncology, 11. [40] Liu, Z., Tong, L., Chen, L., Zhou, F., Jiang, Z., Zhang, Q., ... & Zhou, H. (2021). CANet: Context aware network for brain glioma segmentation. IEEE Transactions on Medical Imaging, 40(7), 1763-1777. [41] Sun, J., Peng, Y., Guo, Y., & Li, D. (2021). Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN. Neurocomputing, 423, 34-45. [42] Rehman, M. U., Cho, S., Kim, J., & Chong, K. T. (2021). BrainSeg-net: Brain tumor MR image segmentation via enhanced encoder–decoder network. Diagnostics, 11(2), 169. [43] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Wang, Y., & Yu, Y. (2020). Exploring task structure for brain tumor segmentation from multi-modality MR images. IEEE Transactions on Image Processing, 29, 9032-9043. [44] Di Ieva, A., Russo, C., Liu, S., Jian, A., Bai, M. Y., Qian, Y., & Magnussen, J. S. (2021). Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario. Neuroradiology, 63(8), 1253-1262. [45] Ali, M. J., Raza, B., & Shahid, A. R. (2021). Multi-level Kronecker Convolutional Neural Network (ML-KCNN) for Glioma Segmentation from Multi-modal MRI Volumetric Data. Journal of Digital Imaging, 34(4), 905-921. [46] Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. [47] He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision (pp. 1026-1034).