[1] 陈东宇， 杨晓雨， 王红心， 等. 基于Joinpoint回归和年龄-时期-队列模型分析中国皮肤癌的长期发病趋势［J］. 中华疾病控制杂志， 2022， 26（7）： 756-765.CHEND Y， YANGX Y， WANGH X， et al. Analysis of secular trends in incidence of skin cancer in China based on Joinpoint regression and age-period-cohort model［J］. Chinese Journal of Disease Control & Prevention， 2022， 26（7）： 756-765.（in Chinese）

[2] 林千里， 张文俊， 汪汇， 等. 皮肤黑色素瘤流行病学及防治研究进展［J］. 中国医药导报， 2019， 16（3）： 28-32.LINQ L， ZHANGW J， WANGH， et al. Research progress on epidemiology， treatment and prevention of malignant melanoma of skin［J］. China Medical Herald， 2019， 16（3）： 28-32.（in Chinese）

[3] N CODELLA, V ROTEMBERG, P TSCHANDL et al. Skin lesion analysis toward melanoma detection 2018： a challenge hosted by the international skin imaging collaboration （ISIC）. ArXiv e-Prints(2019).

[4] 梁礼明， 周珑颂， 冯骏， 等. 基于高分辨率复合网络的皮肤病变分割［J］. 光学 精密工程， 2022， 30（16）： 2021-2038. doi: 10.37188/ope.20223016.2021LIANGL M， ZHOUL S， FENGJ， et al. Skin lesion segmentation based on high-resolution composite network［J］. Opt. Precision Eng.， 2022， 30（16）： 2021-2038.（in Chinese）. doi: 10.37188/ope.20223016.2021

[5] E SHELHAMER, J LONG, T DARRELL. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

[6] O RONNEBERGER, P FISCHER, T BROX. U-Net： convolutional networks for biomedical image segmentation. October 5, 234-241(9).

[7] S M PANG, C L PANG, L ZHAO et al. SpineParseNet： spine parsing for volumetric MR image by a two-stage segmentation framework with semantic image representation. IEEE Transactions on Medical Imaging, 40, 262-273(2021).

[8] Z W ZHOU, M M R SIDDIQUEE, N TAJBAKHSH et al. UNet： redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 39, 1856-1867(2020).

[9] O OKTAY, J SCHLEMPER, L LE FOLGOC et al. Attention U-net： learning where to look for the pancreas. ArXiv e-Prints(2018).

[10] R GU, G T WANG, T SONG et al. CA-net： comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Transactions on Medical Imaging, 40, 699-711(2021).

[11] A R FEYJIE, R AZAD, M PEDERSOLI et al. Semi-Supervised Few-Shot Learning for Medical Image Segmentation. arXiv, 2003-08462(2020). http：//arxiv.org/abs/2003.08462.pdf

[12] A DOSOVITSKIY, L BEYER, A KOLESNIKOV et al. An image is worth 16x16 words： transformers for image recognition at scale. ArXiv e-Prints(2020).

[13] J N CHEN, Y Y LU, Q H YU et al. TransUNet： Transformers Make Strong Encoders for Medical Image Segmentation. arXiv, 2102-04306(2021). http：//arxiv.org/abs/2102.04306.pdf

[14] Y D ZHANG, H Y LIU, Q HU. TransFuse： Fusing Transformers and CNNs for Medical Image Segmentation. Medical Image Computing and Computer Assisted Intervention-MICCAI 2021, 14-24(2021).

[15] H Y WANG, Y K ZHU, B GREEN et al. Computer Vision-ECCV 2020. Axial-DeepLab： Stand-Alone Axial-Attention for Panoptic Segmentation, 108-126(2020).

[16] J HU, L SHEN, S ALBANIE et al. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[17] Q L WANG, B G WU, P F ZHU et al. ECA-Net： efficient channel attention for deep convolutional neural networks, 11531-11539(2020).

[18] F MILLETARI, N NAVAB, S A AHMADI. V-Net： fully convolutional neural networks for volumetric medical image segmentation, 565-571(2016).

[19] D GUTMAN, N C F CODELLA, E CELEBI et al. Skin lesion analysis toward melanoma detection： a challenge at the international symposium on biomedical imaging （ISBI） 2016， hosted by the international skin imaging collaboration （ISIC）. ArXiv e-Prints(2016).

[20] P TSCHANDL, C ROSENDAHL, H KITTLER. The HAM10000 dataset， a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data, 5, 180161(2018).

[21] 徐昌佳， 易见兵， 曹锋， 等. 采用DoubleUNet网络的结直肠息肉分割算法［J］. 光学 精密工程， 2022， 30（8）： 970-983. doi: 10.37188/ope.20223008.0970XUC J， YIJ B， CAOF， et al. Colorectal polyp segmentation algorithm using DoubleUNet network［J］. Opt. Precision Eng.， 2022， 30（8）： 970-983.（in Chinese）. doi: 10.37188/ope.20223008.0970

[22] 孙军梅， 葛青青， 李秀梅， 等. 一种具有边缘增强特点的医学图像分割网络［J］. 电子与信息学报， 2022， 44（5）： 1643-1652. doi: 10.11999/JEIT210784SUNJ M， GEQ Q， LIX M， et al. A medical image segmentation network with boundary enhancement［J］. Journal of Electronics & Information Technology， 2022， 44（5）： 1643-1652.（in Chinese）. doi: 10.11999/JEIT210784

[23] J M J VALANARASU, I HACIHALILOGLU et al. Medical Transformer： Gated Axial-Attention for Medical Image Segmentation. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 36-46(2021).

[24] J M J VALANARASU, V M PATEL. UNeXt： Mlp-Based Rapid Medical Image Segmentation Network. Proceedings, 23-33(2022).