目的建立基于高光谱成像和深度学习的转移淋巴结癌细胞检测系统。收集45例口腔鳞状细胞癌（OSCC）患者转移淋巴结的连续切片。建立改进的ResUNet算法进行深度学习，分析癌细胞与淋巴细胞、肿瘤组织与正常组织之间的光谱曲线差异，发现可以根据转移淋巴结中的癌细胞、淋巴细胞和红细胞进行区分。高光谱图像，总体精度（OA）为87.30%，平均精度（AA）为85.46%。高光谱图像和深度学习可以识别癌变区域，平均交集（IOU）和准确率分别为0.6253和0.7692。本研究表明基于深度学习的高光谱技术可以识别OSCC转移淋巴结中的肿瘤组织，实现病理诊断准确率高，工作效率高，减轻工作负担。但这些只是仅限于小样本的初步结果。© 2024 Wiley periodicals LLC。

To establish a system based on hyperspectral imaging and deep learning for the detection of cancer cells in metastatic lymph nodes.The continuous sections of metastatic lymph nodes from 45 oral squamous cell carcinoma (OSCC) patients were collected. An improved ResUNet algorithm was established for deep learning to analyze the spectral curve differences between cancer cells and lymphocytes, and that between tumor tissue and normal tissue.It was found that cancer cells, lymphocytes, and erythrocytes in the metastatic lymph nodes could be distinguished basing hyperspectral image, with overall accuracy (OA) as 87.30% and average accuracy (AA) as 85.46%. Cancerous area could be recognized by hyperspectral image and deep learning, and the average intersection over union (IOU) and accuracy were 0.6253 and 0.7692, respectively.This study indicated that deep learning-based hyperspectral techniques can identify tumor tissue in OSCC metastatic lymph nodes, achieving high accuracy of pathological diagnosis, high work efficiency, and reducing work burden. But these are preliminary results limited to a small sample.© 2024 Wiley Periodicals LLC.