[1] WEI Z H, SHI F, SONG H et al. Attentive boundary aware network for multi-scale skin lesion segmentation with adversarial training[J]. Multimedia Tools and Applications, 79, 27115-27136(2020).

[2] [2] 2任凤雷， 何昕， 魏仲慧， 等. 基于DeepLabV3+与超像素优化的语义分割［J］. 光学 精密工程， 2019， 27（12）： 2722-2729. doi: 10.3788/ope.20192712.2722RENF L， HEX， WEIZ H， et al. Semantic segmentation based on DeepLabV3+ and superpixel optimization［J］. Opt. Precision Eng.， 2019， 27（12）： 2722-2729.（in Chinese）. doi: 10.3788/ope.20192712.2722

[3] [3] 3秦传波， 宋子玉， 曾军英， 等. 联合多尺度和注意力-残差的深度监督乳腺癌分割［J］. 光学 精密工程， 2021， 29（4）： 877-895. doi: 10.37188/OPE.20212904.0877QINC B， SONGZ Y， ZENGJ Y， et al. Deeply supervised breast cancer segmentation combined with multi-scale and attention-residuals［J］. Opt. Precision Eng.， 2021， 29（4）： 877-895.（in Chinese）. doi: 10.37188/OPE.20212904.0877

[4] ZHAO C, SHUAI R J, MA L et al. Segmentation of skin lesions image based on U-Net + +[J]. Multimedia Tools and Applications, 81, 8691-8717(2022).

[5] GARG S, JINDAL B. Skin lesion segmentation using k-mean and optimized fire fly algorithm[J]. Multimedia Tools and Applications, 80, 7397-7410(2021).

[6] [6] 6邹永宁， 张智斌， 李琦， 等. 基于Hessian矩阵和支持向量机的CT图像裂纹分割［J］. 光学 精密工程， 2021， 29（10）： 2517-2527. doi: 10.37188/OPE.2021.0349ZOUY N， ZHANGZ B， LIQ， et al. Crack detection and segmentation in CT images using Hessian matrix and support vector machine［J］. Opt. Precision Eng.， 2021， 29（10）： 2517-2527.（in Chinese）. doi: 10.37188/OPE.2021.0349

[7] RONNEBERGER O, FISCHER P, BROX T. U-net： convolutional networks for biomedical image segmentation[C], 234-241(2015).

[8] SUN K, ZHAO Y, JIANG B R et al. High-resolution representations for labeling pixels and regions[webpage]. 2019： arXiv(1904). https：//arxiv.org/abs/1904.04514

[9] RADMAN A, SALLAM A, SUANDI S A. Deep residual network for face sketch synthesis[J]. Expert Systems With Applications, 190, 115980(2022).

[10] BAGHERSALIMI S, BOZORGTABAR B, SCHMID-SAUGEON P et al. DermoNet： densely linked convolutional neural network for efficient skin lesion segmentation[J]. EURASIP Journal on Image and Video Processing, 1-10(2019).

[11] WEI Z H, SONG H, CHEN L et al. Attention-based DenseUnet network with adversarial training for skin lesion segmentation[J]. IEEE Access, 7, 136616-136629(2019).

[12] SARKER M M K, RASHWAN H A, AKRAM F et al. SLSDeep： skin lesion segmentation based on dilated residual and pyramid pooling networks[C], 21-29(2018).

[13] [13] 13孟颖， 田启川， 吴施瑶. 基于U型网络复合特征的视网膜血管分割方法［J］. 计算机应用与软件， 2021， 38（8）： 227-232， 267. doi: 10.3969/j.issn.1000-386x.2021.08.035MENGY， TIANQ C， WUS Y. Retinal vessel segmentation method of composite feature based on u-net［J］. Computer Applications and Software， 2021， 38（8）： 227-232， 267.（in Chinese）. doi: 10.3969/j.issn.1000-386x.2021.08.035

[14] YANG B, BENDER G, LE Q V et al. CondConv： conditionally parameterized convolutions for efficient inference[webpage]. 2019： arXiv(1904). https：//arxiv.org/abs/1904.04971

[15] SINGH S, KRISHNAN S. Filter response normalization layer： eliminating batch dependence in the training of deep neural networks[C], 11234-11243(2020).

[16] HUANG G, LIU Z, VAN DER MAATEN L et al. Densely connected convolutional networks[C], 4700-4708(2017).

[17] HOU Q B, ZHANG L, CHENG M M et al. Strip pooling： rethinking spatial pooling for scene parsing[C], 4002-4011(2020).

[18] ZHAO H S, SHI J P, QI X J et al. Pyramid scene parsing network[C], 6230-6239(2017).

[19] [19] 19汪水源， 侯志强， 王囡， 等. 基于自适应模板更新与多特征融合的视频目标分割算法［J］. 光电工程， 2021， 48（10）： 210193.WANGS Y， HOUZ Q， WANGN， et al. Video object segmentation algorithm based on adaptive template updating and multi-feature fusion［J］. Opto-Electronic Engineering， 2021， 48（10）： 210193.（in Chinese）

[20] SALEHI S S M, ERDOGMUS D, GHOLIPOUR A. Tversky loss function for image segmentation using 3D fully convolutional deep networks[C], 379-387(2017).

[21] GUTMAN D, CODELLA N C F, CELEBI E 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）[J]. arXiv preprint arXive：1605, 01397(2016).

[22] CODELLA N C F, GUTMAN D, CELEBI E et al. Skin lesion analysis toward melanoma detection： A challenge at the 2017 international symposium on biomedical imaging （ISBI）， hosted by the international skin imaging collaboration （ISIC）[C], 168-172(2018).

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

[24] MENDONÇA T, FERREIRA P M, MARQUES J S et al. PH₂ - A dermoscopic image database for research and benchmarking[C], 5437-5440(2013).

[25] QAMAR S, AHMAD P, SHEN L L. Dense encoder-decoder-based architecture for skin lesion segmentation[J]. Cognitive Computation, 13, 583-594(2021).

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

[27] [27] 27杨国亮， 洪志阳， 王志元， 等. 基于改进全卷积网络的皮肤病变图像分割［J］. 计算机工程与设计， 2018， 39（11）： 3500-3505.YANGG L， HONGZ Y， WANGZ Y， et al. Image segmentation of skin lesions based on improved fully convolution network［J］. Computer Engineering and Design， 2018， 39（11）： 3500-3505.（in Chinese）

[28] DONG Y Y, WANG L J, CHENG S L et al. FAC-net： feedback attention network based on context encoder network for skin lesion segmentation[J]. Sensors （Basel， Switzerland）, 21, 5172(2021).

[29] MIRIKHARAJI Z, IZADI S, KAWAHARA J et al. Deep auto-context fully convolutional neural network for skin lesion segmentation[C], 877-880(2018).

[30] TONG X Z, WEI J Y, SUN B et al. ASCU-net： attention gate， spatial and channel attention U-net for skin lesion segmentation[J]. Diagnostics （Basel， Switzerland）, 11, 501(2021).

[31] NASR-ESFAHANI E, RAFIEI S, JAFARI M H et al. Dense pooling layers in fully convolutional network for skin lesion segmentation[J]. Computerized Medical Imaging and Graphics, 78, 101658(2019).

[32] XIE F Y, YANG J W, LIU J et al. Skin lesion segmentation using high-resolution convolutional neural network[J]. Computer Methods and Programs in Biomedicine, 186, 105241(2020).

[33] TANG Y J, YANG F, YUAN S F et al. A multi-stage framework with context information fusion structure for skin lesion segmentation[C], 1407-1410(2019).

[34] VESAL S, RAVIKUMAR N, MAIER A. SkinNet： a deep learning framework for skin lesion segmentation[C], 1-3(2018).

[35] ÜNVER H M, AYAN E. Skin lesion segmentation in dermoscopic images with combination of YOLO and GrabCut algorithm[J]. Diagnostics （Basel， Switzerland）, 9, 72(2019).

[36] LEI B Y, XIA Z M, JIANG F et al. Skin lesion segmentation via generative adversarial networks with dual discriminators[J]. Medical Image Analysis, 64, 101716(2020).

[37] JIN Q G, CUI H, SUN C M et al. Cascade knowledge diffusion network for skin lesion diagnosis and segmentation[J]. Applied Soft Computing, 99, 106881(2021).

[38] RAMADAN R. CU-net： a new improved multi-input color U-net model for skin lesion semantic segmentation[J]. IEEE Access, 10, 15539-15564(2022).

[39] WU H S, PAN J Q, LI Z Y et al. Automated skin lesion segmentation via an adaptive dual attention module[J]. IEEE Transactions on Medical Imaging, 40, 357-370(2021).

[40] HU K, LU J, LEE D J et al. AS-net： attention synergy network for skin lesion segmentation[J]. Expert Systems With Applications, 201, 117112(2022).