[1] [1] 李伟， 吕蒙， 陈天虹， 等. 高光谱图像在生物医学中的应用［J］. 中国图象图形学报， 2021， 26（8）： 1764-1785.LIW， LYU M， CHENT H， et al. Application of a hyperspectral image in medical field： a review［J］. Journal of Image and Graphics， 2021， 26（8）： 1764-1785.（in Chinese）

[2] [2] CHENY R， WANGY N， ZHANGH， et al. 医学高光谱显微成像与智能分析关键技术研究及应用［J］. AI-View， 2022（3）： 22-32.（in Chinese）陈煜嵘， 王耀南， 张辉， 等. 医学高光谱显微成像与智能分析关键技术研究及应用［J］. 人工智能， 2022（3）： 22-32.

[3] UL A， REHMAN, UL A， REHMAN. A review of the medical hyperspectral imaging systems and unmixing algorithms’ in biological tissues. Photodiagnosis and Photodynamic Therapy, 33, 102165(2021).

[4] [4] 闫敬文， 陈宏达， 刘蕾. 高光谱图像分类的研究进展［J］. 光学 精密工程， 2019， 27（3）： 680-693. doi: 10.3788/ope.20192703.0680YANJ W， CHENH， LIUL. Overview of hyperspectral image classification ［J］. Opt. Precision Eng.， 2019， 27（3）： 680-693. （in Chinese）. doi: 10.3788/ope.20192703.0680

[5] [5] 宋蓓蓓， 马穗娜， 何帆， 等. Res2-Unet深度学习网络的RGB-高光谱图像重建［J］. 光学 精密工程， 2022， 30（13）： 1606-1619. doi: 10.37188/OPE.2021.0433SONGB B， MAS N， HEF， et al. Hyperspectral reconstruction from RGB images based on Res2-Unet deep learning network［J］. Opt. Precision Eng.， 2022， 30（13）： 1606-1619.（in Chinese）. doi: 10.37188/OPE.2021.0433

[6] [6] 时旭， 李远， 黄鸿. 面向高光谱显微图像血细胞分类的空-谱可分离卷积神经网络［J］. 光学 精密工程， 2022， 30（8）：960-969. doi: 10.37188/ope.20223008.0960SHIX， LIY， HUANGH. Spatial-spectral separable convolutional neural network for cell classification of hyperspectral microscopic images［J］. Opt. Precision Eng.， 2022， 30（8）：960-969.（in Chinese）. doi: 10.37188/ope.20223008.0960

[7] [7] 郑少佳， 邱崧， 李庆利， 等. 傅里叶变换通道注意力网络的胆管癌高光谱图像分割［J］. 中国图象图形学报， 2021， 26（8）：1836-1846. doi: 10.11834/jig.210207ZHENGS J， QIUS， LIQ L， et al. Fourier transform channel attention network for cholangiocarcinoma hyperspectral image segmentation［J］. Journal of Image and Graphics， 2021， 26（8）：1836-1846.（in Chinese）. doi: 10.11834/jig.210207

[8] H FABELO, S ORTEGA, A SZOLNA et al. In-vivo hyperspectral human brain image database for brain cancer detection. IEEE Access, 7, 39098-39116(2019).

[9] J BØTKER, J X WU, J RANTANEN. Data Handling in Science and Technology(2020).

[10] YIFAN， DUAN, YIFAN， DUAN. Leukocyte classification based on spatial and spectral features of microscopic hyperspectral images. Optics & Laser Technology, 112, 530-538(2019).

[11] L RUIZ, A MARTÍN, G URBANOS et al. Multiclass brain tumor classification using hyperspectral imaging and supervised machine learning, 1-6(2020).

[12] E J M BALTUSSEN, E N D KOK, S G B DE KONING et al. Hyperspectral imaging for tissue classification， a way toward smart laparoscopic colorectal surgery. Journal of Biomedical Optics, 24(2019).

[13] Q HUANG, W LI, B C ZHANG et al. Blood cell classification based on hyperspectral imaging with modulated Gabor and CNN. IEEE Journal of Biomedical and Health Informatics, 24, 160-170(2020).

[14] X L WEI, W LI, M M ZHANG et al. Medical hyperspectral image classification based on end-to-end fusion deep neural network. IEEE Transactions on Instrumentation and Measurement, 68, 4481-4492(2019).

[15] Q ZHANG, Y WANG, S QIU et al. 3D-PulCNN： pulmonary cancer classification from hyperspectral images using convolution combination unit based CNN. Journal of Biophotonics, 14(2021).

[16] B L HU, J DU, Z F ZHANG et al. Tumor tissue classification based on micro-hyperspectral technology and deep learning. Biomedical Optics Express, 10, 6370(2019).

[17] A DOSOVITSKIY, L BEYER, A KOLESNIKOV et al. An Image is Worth. arXiv, 2010-11929(2020). https：//arxiv.org/abs/2010.11929.pdf

[18] Z H ZHOU, S QIU, Y WANG et al. Swin-spectral transformer for cholangiocarcinoma hyperspectral image segmentation, 1-6(23).

[19] N Y LI, J Q XUE, S JIA. Spectral context-aware transformer for cholangiocarcinoma hyperspectral image segmentation, 209-213(9).

[20] Y LI, X SHI, L P YANG et al. MC-GAT： multi-layer collaborative generative adversarial transformer for cholangiocarcinoma classification from hyperspectral pathological images. Biomedical Optics Express, 13, 5794-5812(2022).

[21] Y S PENG, Y W ZHANG, B TU et al. Spatial-spectral transformer with cross-attention for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15(2022).

[22] E OUYANG, B LI, W J HU et al. When multigranularity meets spatial–spectral attention： a hybrid transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-18(2023).

[23] B LIU, A Z YU, K L GAO et al. DSS-TRM： deep spatial-spectral transformer for hyperspectral image classification. European Journal of Remote Sensing, 55, 103-114(2022).

[24] S K ROY, G KRISHNA, S R DUBEY et al. HybridSN： exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 17, 277-281(2020).

[25] Z L ZHONG, J LI, Z M LUO et al. Spectral-spatial residual network for hyperspectral image classification： a 3-D deep learning framework. IEEE Transactions on Geoscience and Remote Sensing, 56, 847-858(2018).

[26] R LI, S Y ZHENG, C X DUAN et al. Classification of hyperspectral image based on double-branch dual-attention mechanism network. Remote Sensing, 12, 582(2020).

[27] D F HONG, Z HAN, J YAO et al. SpectralFormer： rethinking hyperspectral image classification with transformers. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15(2022).

[28] L SUN, G R ZHAO, Y H ZHENG et al. Spectral–spatial feature tokenization transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-14(2022).

[29] J J ZHANG, Z MENG, F ZHAO et al. Convolution transformer mixer for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 19, 1-5(2022).