Master class in Computer Science - Digital Image Processing & Computer Vision (Xử lý ảnh số và Video số - Thị giác máy tính)
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[1] (2D UNet) Ronneberger, O.; Fischer, P.; Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 5–9 October 2015; pp. 234–241.
[2] (3D UNet) Çiçek, Ö.; Abdulkadir, A.; Lienkamp, S.S.; Brox, T.; Ronneberger, O. 3D U-Net: Learning dense volumetric segmentation from sparse annotation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece, 17–21 October 2016; pp. 424–432.
[3] (VNet) Milletari, F.; Navab, N.; Ahmadi, S.-A. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA, 25–28 October 2016; pp. 565–571.
[4] (Weighted Res-UNet) Xiao, X.; Lian, S.; Luo, Z.; Li, S. Weighted Res-UNet for high-quality retina vessel segmentation. In Proceedings of the 9th International Conference on Information Technology in Medicine and Education (ITME), Hangzhou, China, 19–21 October 2018; pp. 327–331.
[5] (Hybrid densely connected UNet) Li, X.; Chen, H.; Qi, X.; Dou, Q.; Fu, C.-W.; Heng, P.A. H-DenseUNet: Hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 2018, 37, 2663–2674.
[6] (MultiResUNet) Ibtehaz, N.; Rahman, M.S. MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 2020, 121, 74–87.
[7] (Attention UNet) Oktay, O.; Schlemper, J.; Folgoc, L.L.; Lee, M.; Heinrich, M.; Misawa, K.; Mori, K.; Mcdonagh, S.; Hammerla, N.Y.; Kainz, B.; et al. Attention u-net: Learning where to look for the pancreas. arXiv 2018, arXiv:1804.03999.
[8] (Non-local UNet) Wang, Z.; Zou, N.; Shen, D.; Ji, S. Non-Local U-Nets for Biomedical Image Segmentation. In Proceedings of the AAAI, New York, NY, USA, 7–12 February 2020; pp. 6315–6322.
[9] Cai, Yutong and Yong Wang. “MA-Unet: An improved version of Unet based on multi-scale and attention mechanism for medical image segmentation.” ArXiv abs/2012.10952 (2020): n. pag.
[10] Chen, Xiaocong, Lina Yao and Yanyan Zhang. “Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images.” ArXiv abs/2004.05645 (2020): n. pag.
[11] Chen, Jieneng, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan Loddon Yuille and Yuyin Zhou. “TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.” ArXiv abs/2102.04306 (2021): n. pag.
[12] Chen, Bingzhi, Yishu Liu, Zheng Zhang, Guangming Lu and David Zhang. “TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation.” ArXiv abs/2107.05274 (2021): n. Pag.
[13] Peiris, H.T.R., Munawar Hayat, Zhaolin Chen, Gary Egan and Mehrtash Harandi. “A Volumetric Transformer for Accurate 3D Tumor Segmentation.” ArXiv abs/2111.13300 (2021): n. pag.
[14] Zhou, Hong-Yu, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang and Yizhou Yu. “nnFormer: Interleaved Transformer for Volumetric Segmentation.” ArXiv abs/2109.03201 (2021): n. pag.
[15] Cheng, Junlong, Chengrui Gao, Chaoqing Wang, Zhang Ming, Yong Yang and Min Zhu. “PL-Net: Progressive Learning Network for Medical Image Segmentation.” ArXiv abs/2110.14484 (2021): n. pag.
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