[1] G LITJENS, T KOOI, B E BEJNORDI et al. A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88(2017).

[2] S PRABHUA, K PRASADA, A ROBELS-KELLY et al. AI-based carcinoma detection and classification using histopathological images: A systematic review. Computers in Biology and Medicine, 144, 105209(2022).

[3] K RAMESH, G K KUMAR, K SWAPNA et al. A review of medical image segmentation algorithms. EAI Endorsed Transactions on Pervasive Health and Technology, 7, e6(2021).

[4] N OTSU. A threshold selection method from gray-level histograms. IEEE Transactions on Systems Man & Cybernetics, 9, 62-66(2007).

[5] L VINCENT, P SOILLE. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 583-598(1991).

[6] Y Y BOYKOV, M P JOLLY. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, 105-112(2001).

[7] Xiaoming HUANG, Fuyun HE, Xiaohu TANG et al. Review on applications of U-Net and its variants in medical image segmentation. Chinese Journal of Biomedical Engineering, 41, 567-576(2022).

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

[9] Z ZHOU, M M R SIDDIQUEE, N TAJBAKHSH et al. UNet++: a nested U-Net architecture for medical image segmentation, 3-11(2018).

[10] N IBTEHAZ, M S RAHMAN. MultiResUNet: Rethinking the u-net architecture for multimodal biomedical image segmentation. Neural Networks, 121, 74-87(2020).

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

[12] J S WANG, Y WANG, X TAO et al. PCA-U-Net based breast cancer nest segmentation from microarray hyperspectral images. Fundamental Research, 1, 631-640(2021).

[13] Hong HUANG, Tao WANG, Yuan LI et al. Cancer pathological segmentation network based on depth feature fusion. Acta Photonica Sinica, 51, 0310001(2022).

[14] B C ZHAO, X CHEN, Z LI et al. Triple U-net: Hematoxylin-aware nuclei segmentation with progressive dense feature aggregation. Medical Image Analysis, 65, 101786(2020).

[15] W ZAMORA, M MENDEZ, S CALDERON-RAMIREZ et al. Enforcing morphological information in fully convolutional networks to improve cell instance segmentation in fluorescence microscopy images, 1, 36-46(2021).

[16] Y FANG, C CHEN, Y YUAN et al. Selective feature aggregation network with area-boundary constraints for polyp segmentation, 302-310(2019).

[17] D P FAN, G P JI, T ZHOU et al. PraNet:parallel reverse attention network for polyp segmentation, 263-273(2020).

[18] S CHEN, C DING, M LIU et al. CPP-Net: context-aware polygon proposal network for nucleus segmentation. IEEE Transactions on Image Processing, 32, 980-994(2023).

[19] R ZHANG, G LI, Z LI et al. Adaptive context selectionfor polyp segmentation, 253-262(2020).

[20] J CHEN, Y LU, Q YU et al. Transunet: transformers make strong encoders for medical image segmentation(2021).

[21] J VALANARASU, P OZA, I HACIHSLILOGLU et al. Medical transformer: gated axial-attention for medical image segmentation, 36-46(2021).

[22] H WANG, P CAO, J WANG et al. UCTransNet: rethinking the skip connections in u-net from a channel-wise perspective with transformer, 36, 2441-2449(2022).

[23] M X TAN, Q V LE. EfficientNet: rethinking model scaling for convolutional neural networks, 691-700(2019).

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

[25] Q WANG, B WU, P ZHU et al. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks, 11531-11539(2020).

[26] Jun YANG, Bozan LI. Semantic segmentation of 3D point cloud based on self-attention feature fusion group convolutional neural network. Optics and Precision Engineering, 30, 840-853(2022).

[27] K SIRINUKUNWATTANA, J P PLUIM, H CHEN et al. Gland segmentation in colon histology images: The glas challenge contest. Medical Image Analysis, 35, 489-502(2021).

[28] N KUMAR, R VERMA, S SHARMA et al. A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Transactions on Medical Imaging, 36, 1550-1560(2017).

[29] N KUMAR, R VERMA, D ANAND et al. A multi-organ nucleus segmentation challenge. IEEE Transactions on Medical Imaging, 39, 1380-1391(2019).