Fu Y, Lei Y, Wang T, Curran WJ, Liu T, Yang X. Deep learning in medical image registration: a review. Phys Med Biol. 2020;65(20):20TR01.
Ter-Sarkisov A. Detection and segmentation of lesion areas in chest CT scans for the prediction of COVID-19. MedRxiv. 2020:2020.10. 23.20218461.
Müller D, Rey IS, Kramer F. Automated chest ct image segmentation of covid-19 lung infection based on 3d u-net. arXiv preprint arXiv:2007.04774. 2020.
Ma J, Wang Y, An X, Ge C, Yu Z, Chen J, et al. Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation. Medical physics. 2021;48(3):1197-210.
Wang Y, Zhang Y, Liu Y, Tian J, Zhong C, Shi Z, et al. Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation. Comput Methods Programs Biomed. 2021;202:106004.
Paluru N, Dayal A, Jenssen HB, Sakinis T, Cenkeramaddi LR, Prakash J, et al. Anam-Net: Anamorphic depth embedding-based lightweight CNN for segmentation of anomalies in COVID-19 chest CT images. IEEE Trans Neural Netw Learn Syst. 2021;32(3):932-46.
Aswathy A, SS VC. Cascaded 3D UNet architecture for segmenting the COVID-19 infection from lung CT volume. Scientific Reports. 2022;12.
Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, et al., editors. Unetr: Transformers for 3d medical image segmentation. Proceedings of the IEEE/CVF winter conference on applications of computer vision; 2022.
Wei C, Ren S, Guo K, Hu H, Liang J. High-resolution Swin transformer for automatic medical image segmentation. Sensors. 2023;23(7):3420.
Radford A, Narasimhan K, Salimans T, Sutskever I. Improving language understanding by generative pre-training. 2018. https://www.mikecaptain.com/resources/pdf/GPT-1.pdf.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Adv Neural Inf Process Syst. 2017.
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. 2020.
Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, Jégou H, editors. Training data-efficient image transformers & distillation through attention. International conference on machine learning; 2021.
Beal J, Kim E, Tzeng E, Park DH, Zhai A, Kislyuk D. Toward transformer-based object detection. arXiv preprint arXiv:2012.09958. 2020.
Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P. SegFormer: Simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Process Syst. 2021;34:12077-90.
Caron M, Touvron H, Misra I, Jégou H, Mairal J, Bojanowski P, et al., editors. Emerging properties in self-supervised vision transformers. Proceedings of the IEEE/CVF international conference on computer vision; 2021.
COVID-19-CT-Seg dataset. https://zenodo.org/record/3757476#.
Morozov SP, Andreychenko AE, Blokhin IA, Gelezhe PB, Gonchar AP, Nikolaev AE, et al. MosMedData: data set of 1110 chest CT scans performed during the COVID-19 epidemic. Digital Diagnostics. 2020;1(1):49-59.
Zhang Q, Ren X, Wei B. Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net. Scientific Reports. 2021;11(1):22854.
Morozov SP, Andreychenko AE, Pavlov NA, Vladzymyrskyy AV, Ledikhova NV, Gombolevskiy VA, et al. Mosmeddata: Chest ct scans with covid-19 related findings dataset. arXiv preprint arXiv:2005.06465. 2020.
Hu J, Shen L, Sun G, editors. Squeeze-and-excitation networks. Proceedings of the IEEE conference on computer vision and pattern recognition; 2018.
Lee S, Lee M. MetaSwin: a unified meta vision transformer model for medical image segmentation. PeerJ Comput Sci. 2024;10:e1762.
Tang Y, Yang D, Li W, Roth HR, Landman B, Xu D, et al., editors. Self-supervised pre-training of swin transformers for 3d medical image analysis. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 2022.
Kundu S, Sundaresan S, editors. Attentionlite: Towards efficient self-attention models for vision. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2021.
Kumar Singh V, Abdel-Nasser M, Pandey N, Puig D. Lunginfseg: Segmenting covid-19 infected regions in lung ct images based on a receptive-field-aware deep learning framework. Diagnostics. 2021;11(2):158.
Zheng R, Zheng Y, Dong-Ye C. Improved 3D U‐Net for COVID‐19 chest CT image segmentation. Sci Program. 2021;2021(1):9999368.
留言 (0)