[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] N COUDRAY, P S OCAMPO, T SAKELLAROPOULOS et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nature Medicine, 10, 1559-1567(2018).
[3] F GHAZNAVI, A EVANS, A MADABHUSHI et al. Digital imaging in pathology: whole-slide imaging and beyond. Annual Review of Pathology-Mechanisms of Disease, 8, 331-359(2013).
[4] Xu JIN, Ke WEN, Guofeng LV et al. Survey on the applications of deep learning to histopathology. Journal of Image and Graphics, 25, 1982-1993(2020).
[5] N OTSU. A threshold selection method from gray-level histograms. IEEE Transactions on Systems Man & Cybernetics, 9, 62-66(2007).
[6] X CHEN, C F LIAN, L WANG et al. Diverse data augmentation for learning image segmentation with cross-modality annotations. Medical Image Analysis, 71, 102060(2021).
[7] 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).
[8] Y Y BOYKOV, M P JOLLY. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, 5-112(2001).
[9] Weixing WANG, Liping TIAN, Yue WANG. Segmentation of cell images based on improved graph MST and skeleton distance mapping. Optics and Precision Engineering, 21, 2464-2472(2013).
[10] J LONG, E SHELHAMER, T DARRELL. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2015).
[11] O RONNEBERGER, P FISCHER, T BROX. U-Net: convolutional networks for biomedical image segmentation, 234-241(2015).
[12] Z ZHOU, M M R SIDDIQUEE, N TAJBAKHSH et al. UNet++: a nested U-Net architecture for medical image segmentation, 3-11(2018).
[13] D C CIRESAN, A GIUSTI, L M GAMBARDELLA et al. Deep neural networks segment neuronal membranes in electron microscopy images, 2, 2843-2851(2012).
[14] Hong HUANG, Rongfei LV, Junli TAO et al. Segmentation of lung nodules in CT images using improved U-Net++. Acta Photonica Sinica, 50, 0210001(2021).
[15] Q LI, X HE, Y WANG et al. Review of spectral imaging technology in biomedical engineering: achievements and challenges. Journal of Biomedical Optics, 18, 100901(2013).
[16] Shaojia ZHENG, Qingli LI, Mei ZHOU et al. Fourier transform channel attention network for cholangiocarcinoma hyperspectral image segmentation. Journal of Image and Graphics, 26, 1836-1846(2021).
[17] X WEI, W LI, M ZHANG et al. Medical hyperspectral image classification based on end-to-end fusion deep neural network. IEEE Transactions on Instrumentation & Measurement, 68, 4481-4492(2019).
[18] Jie SONG, Liang XIAO, Zhichao LIAN et al. Overview and prospect of deep learning for image segmentation in digital pathology. Journal of Software, 32, 1427-1460(2021).
[19] M X TAN, Q V LE. EfficientNet: rethinking model scaling for convolutional neural networks, 691-700(2019).
[20] J HU, L SHEN, G SUN et al. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).
[23] V BADRINARAYANAN, A KENDALL, R CIPOLLA. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence, 39, 2481-2495(2017).
[24] L C CHEN, Y ZHU, G PAPANDREOU et al. Encoder-decoder with atrous separable convolution for semantic image segmentation(2018).
[25] 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(2020).
[26] J CHEN, Y LU, Q YU et al. TransUNet: transformers make strong encoders for medical image segmentation(2021).