Abstract：
OBJECTIVE: To establish a system based on hyperspectral imaging and deep learning for the detection of cancer cells in metastatic lymph nodes.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
目的：建立基于高光谱成像和深度学习的转移性淋巴结癌细胞检测系统。
方法：收集45例口腔鳞状细胞癌（OSCC）患者的转移淋巴结连续切片。建立了一种改进的ResUNet算法,用于深度学习分析癌细胞和淋巴细胞之间的光谱曲线差异,在肿瘤组织和正常组织之间。
结果：发现癌细胞，淋巴细胞，转移淋巴结中的红细胞可以根据高光谱图像进行区分，总体准确度(OA)为87.30%,平均准确度(AA)为85.46%。可以通过高光谱图像和深度学习来识别癌变区域，平均交集（IOU）和准确性分别为0.6253和0.7692。
结论：这项研究表明，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，实现病理诊断的高精度，工作效率高,减轻工作负担。但这些初步结果仅限于小样本。
方法：收集45例口腔鳞状细胞癌（OSCC）患者的转移淋巴结连续切片。建立了一种改进的ResUNet算法,用于深度学习分析癌细胞和淋巴细胞之间的光谱曲线差异,在肿瘤组织和正常组织之间。
结果：发现癌细胞，淋巴细胞，转移淋巴结中的红细胞可以根据高光谱图像进行区分，总体准确度(OA)为87.30%,平均准确度(AA)为85.46%。可以通过高光谱图像和深度学习来识别癌变区域，平均交集（IOU）和准确性分别为0.6253和0.7692。
结论：这项研究表明，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，实现病理诊断的高精度，工作效率高,减轻工作负担。但这些初步结果仅限于小样本。
