[1] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[2] Çiçek Ö, Abdulkadir A, Lienkamp S S et al. 3D U-net: learning dense volumetric segmentation from sparse annotation[M]. Ourselin S, Joskowicz L, Sabuncu M R, et al. Medical image computing and computer-assisted intervention-MICCAI 2016, 9901, 424-432(2016).

[3] Milletari F, Navab N, Ahmadi S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[C], 565-571(2016).

[4] Isensee F, Jäger P F, Kohl S A A et al. Automated Design of Deep Learning Methods for Biomedical Image Segmentation[EB/OL]. https://arxiv.org/abs/1904.08128

[5] Xiao X, Lian S, Luo Z M et al. Weighted res-UNet for high-quality retina vessel segmentation[C], 327-331(2018).

[6] Xiao Z Y, Du N M, Liu J J et al. SR-Net: a sequence offset fusion net and refine net for undersampled multislice MR image reconstruction[J]. Computer Methods and Programs in Biomedicine, 202, 105997(2021).

[7] Xiao Z Y, He K H, Liu J J et al. Multi-view hierarchical split network for brain tumor segmentation[J]. Biomedical Signal Processing and Control, 69, 102897(2021).

[8] Liu Y M, Xiao Z Y. Automatic segmentation algorithm of liver tumor based on feature fusion[J]. Laser & Optoelectronics Progress, 58, 1417001(2021).

[9] Oktay O, Schlemper J, Folgoc L L et al. Attention U-net: learning where to look for the pancreas[EB/OL]. https://arxiv.org/abs/1804.03999

[10] Hu C, Kang G X, Hou B B et al. Acu-net: a 3D attention context U-Net for multiple sclerosis lesion segmentation[C], 1384-1388(2020).

[11] Zhang W X, Zhu Z C, Zhang Y H et al. Cell image segmentation method based on residual block and attention mechanism[J]. Acta Optica Sinica, 40, 1710001(2020).

[12] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[EB/OL]. https://arxiv.org/abs/1706.03762v1

[13] Chen J N, Lu Y Y, Yu Q H et al. TransUNet: transformers make strong encoders for medical image segmentation[EB/OL]. https://arxiv.org/abs/2102.04306

[14] Cao H, Wang Y Y, Chen J et al. Swin-UNet: UNet-like pure transformer for medical image segmentation[EB/OL]. https://arxiv.org/abs/2105.05537

[15] Gao Y H, Zhou M, Metaxas D N. UTNet: a hybrid transformer architecture for medical image segmentation[M]. de Bruijne M, Cattin P C, Cotin S, et al. Medical image computing and computer assisted intervention-MICCAI 2021. Lecture notes in computer science, 12903, 61-71(2021).

[16] Touvron H, Cord M, Douze M et al. Training data-efficient image transformers & distillation through attention[EB/OL]. https://arxiv.org/abs/2012.12877v2

[17] Valanarasu J M J, Oza P, Hacihaliloglu I et al. Medical transformer: gated axial-attention for medical image segmentation[EB/OL]. https://arxiv.org/abs/2102.1066 2

[18] Ho J, Kalchbrenner N, Weissenborn D et al. Axial attention in multidimensional transformers[EB/OL]. https://arxiv.org/abs/1912.12180

[19] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[20] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C], 15523970(2015).

[21] Lou A G, Guan S Y, Loew M. DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation[J]. Proceedings of SPIE, 11596, 758-768(2021).

[22] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth 16x16 words: transformers for image recognition at scale[EB/OL]. https://arxiv.org/abs/2010.11929

[23] Liu Z, Lin Y T, Cao Y et al. Swin transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(2021).

[24] Han K, Xiao A, Wu E H et al. Transformer in transformer[EB/OL]. https://arxiv.org/abs/2103.00112

[25] Rivière B, Hönig W, Yue Y S et al. GLAS: global-to-local safe autonomy synthesis for multi-robot motion planning with end-to-end learning[J]. IEEE Robotics and Automation Letters, 5, 4249-4256(2020).

[26] Jha D, Smedsrud P H, Riegler M A et al. Kvasir-SEG: A segmented polyp dataset[M]. Ro Y M, Cheng W H, Kim J, et al. MultiMedia modeling. Lecture notes in computer science, 11962, 451-462(2019).

[27] Loshchilov I, Hutter F. Fixing weight decay regularization in Adam[EB/OL]. https://arxiv.org/abs/1711.05101v1

[28] Valanarasu J M J, Sindagi V A. et al. KiU-Net: towards accurate segmentation of biomedical images using over-complete representations[EB/OL]. https://arxiv.org/abs/2006.04878v2

[29] Graham S, Epstein D, Rajpoot N. Rota-net: rotation equivariant network for simultaneous gland and lumen segmentation in colon histology images[M]. Reyes-Aldasoro C C, Janowczyk A, Veta M, et al. Digital pathology, 11435, 109-116(2019).

[30] Pinckaers H, Litjens G. Neural ordinary differential equations for semantic segmentation of individual colon glands[EB/OL]. https://arxiv.org/abs/1910.10470

[31] Jha D, Ali S, Tomar N K et al. Real-time polyp detection, localization and segmentation in colonoscopy using deep learning[J]. IEEE Access, 9, 40496-40510(2021).

[32] Bernal J, Sánchez F J, Fernández-Esparrach G et al. WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians[J]. Computerized Medical Imaging and Graphics, 43, 99-111(2015).