Sung, H., et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin; 2021. 71(3): 209–249; https://doi.org/10.3322/caac.21660.

Li, D., et al. RTAU-Net: A novel 3D rectal tumor segmentation model based on dual path fusion and attentional guidance. Computer Methods and Programs in Biomedicine; 2023. 242: 107842; https://doi.org/10.1016/j.cmpb.2023.107842.

Yin, S., et al. Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks. Medical Image Analysis; 2020. 60: 101602; https://doi.org/10.1016/j.media.2019.101602.

Ronneberger, O., P. Fischer, and T. Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. 2015. Cham: Springer International Publishing.

Krizhevsky, A., I. Sutskever, and G.E. Hinton. ImageNet classification with deep convolutional neural networks. Communications of the ACM; 2017. 60(6): 84–90; https://doi.org/10.1145/3065386.

Zhou, Z., et al. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. 2018. Cham: Springer International Publishing.

Chen, L.-C., et al. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. in Computer Vision – ECCV 2018. 2018. Cham: Springer International Publishing.

Vaswani, A., et al. Attention is All you Need. in Neural Information Processing Systems. 2017.

Dosovitskiy, A., et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. 2020. abs/2010.11929.

Liu, Z., et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. in 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 2021.

Li, X., et al. Selective Kernel Networks. in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019.

Azad, R., et al. DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation. 2022. arXiv:2212.13504; https://doi.org/10.48550/arXiv.2212.13504.

Chen, J., et al. TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. 2021. abs/2102.04306; https://arxiv.org/abs/2102.04306.

Cao, H., et al. Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation. 2021. arXiv:2105.05537; https://doi.org/10.48550/arXiv.2105.05537.

Gu, Z., et al. CE-Net: Context Encoder Network for 2D Medical Image Segmentation. IEEE Transactions on Medical Imaging; 2019. 38(10): 2281–2292; https://doi.org/10.1109/TMI.2019.2903562.

Li, D., et al. Improved U-Net based on contour prediction for efficient segmentation of rectal cancer. Computer Methods and Programs in Biomedicine; 2022. 213: 106493; https://doi.org/10.1016/j.cmpb.2021.106493.

Yuqian, W., M. Jianwei, and Z. Shaofei. U -Net based on Feature Fusion for Rectal Cancer Image Segmentation. in 2021 11th International Conference on Information Technology in Medicine and Education (ITME). 2021.

Rao, Y., et al. Using Channel Concatenation and Lightweight Atrous Convolution in U-net for Accurate Rectal Cancer Segmentation. in 2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). 2021.

LeBlanc, J.K. Imaging and management of rectal cancer. Nature Clinical Practice Gastroenterology & Hepatology; 2007. 4(12): 665–676; https://doi.org/10.1038/ncpgasthep0977.

Chen, G., et al. AAU-Net: An Adaptive Attention U-Net for Breast Lesions Segmentation in Ultrasound Images. IEEE Transactions on Medical Imaging; 2023. 42(5): 1289–1300; https://doi.org/10.1109/TMI.2022.3226268.

Bi, H., et al. BPAT-UNet: Boundary preserving assembled transformer UNet for ultrasound thyroid nodule segmentation. Computer Methods and Programs in Biomedicine; 2023. 238: 107614; https://doi.org/10.1016/j.cmpb.2023.107614.

Yi, S., et al. Diagnosis of rectal cancer based on the Xception-MS network. Physics in Medicine & Biology; 2022. 67(19): 195002; https://doi.org/10.1088/1361-6560/ac8f11.

Ding, Y., et al. MallesNet: A multi-object assistance based network for brachial plexus segmentation in ultrasound images. Medical Image Analysis; 2022. 80: 102511; https://doi.org/10.1016/j.media.2022.102511.

Russell, B.C., et al. LabelMe: A Database and Web-Based Tool for Image Annotation. International Journal of Computer Vision; 2008. 77(1): 157–173; https://doi.org/10.1007/s11263-007-0090-8.

Lee, J., et al. Reducing the Model Variance of a Rectal Cancer Segmentation Network. IEEE Access; 2019. 7: 182725-182733; https://doi.org/10.1109/ACCESS.2019.2960371.

Article  Google Scholar 

Buslaev, A., et al. Albumentations: Fast and Flexible Image Augmentations. Information, 2020. 11; https://doi.org/10.3390/info11020125.

Loshchilov, I. and F. Hutter. Decoupled Weight Decay Regularization. in International Conference on Learning Representations. 2017.

El-Nouby, A., et al. XCiT: Cross-Covariance Image Transformers. 2021. arXiv:2106.09681; https://doi.org/10.48550/arXiv.2106.09681.

Ross, A. and V.L. Willson, Paired Samples T-Test, in Basic and Advanced Statistical Tests: Writing Results Sections and Creating Tables and Figures, A. Ross and V.L. Willson, Editors. 2017, SensePublishers: Rotterdam. p. 17-19.

Chapter  Google Scholar 

Haynes, W., Bonferroni Correction, in Encyclopedia of Systems Biology, W. Dubitzky, et al., Editors. 2013, Springer New York: New York, NY. p. 154–154.

Diener, M.J., Cohen's d, in The Corsini Encyclopedia of Psychology. 2010. p. 1–1.

He, K., et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. in 2015 IEEE International Conference on Computer Vision (ICCV). 2015.

Badrinarayanan, V., A. Kendall, and R. Cipolla. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence; 2017. 39(12): 2481–2495; https://doi.org/10.1109/TPAMI.2016.2644615.