The classification of colorectal cancer (CRC) lymph node metastasis (LNM) is a vital clinical issue related to recurrence and design of treatment plans. However, it remains unclear which method is effective in automatically classifying CRC LNM. Hence, this study compared the performance of existing classification methods, i.e., machine learning, deep learning, and deep transfer learning, to identify the most effective method. A total of 3,364 samples (1,646 positive and 1,718 negative) from Harbin Medical University Cancer Hospital were collected. All patches were manually segmented by experienced radiologists, and the image size was based on the lesion to be intercepted. Two classes of global features and one class of local features were extracted from the patches. These features were used in eight machine learning algorithms, while the other models used raw data. Experiment results showed that deep transfer learning was the most effective method with an accuracy of 0.7583 and an area under the curve of 0.7941. Furthermore, to improve the interpretability of the results from the deep learning and deep transfer learning models, the classification heat-map features were used, which displayed the region of feature extraction by superposing with raw data. The research findings are expected to promote the use of effective methods in CRC LNM detection and hence facilitate the design of proper treatment plans.

中文翻译：

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结直肠癌淋巴结转移图像分类的不同机器学习和深度学习方法

结直肠癌（CRC）淋巴结转移（LNM）的分类是与复发和治疗计划设计相关的重要临床问题。然而，目前尚不清楚哪种方法能够有效地自动分类 CRC LNM。因此，本研究比较了现有分类方法（即机器学习、深度学习和深度迁移学习）的性能，以确定最有效的方法。共采集哈尔滨医科大学肿瘤医院样本3364份（阳性1646份，阴性1718份）。所有斑块均由经验丰富的放射科医生手动分割，图像大小基于要截取的病变。从补丁中提取两类全局特征和一类局部特征。这些特征被用于八种机器学习算法，而其他模型则使用原始数据。实验结果表明，深度迁移学习是最有效的方法，精度为0.7583，曲线下面积为0.7941。此外，为了提高深度学习和深度迁移学习模型结果的可解释性，使用了分类热图特征，它通过与原始数据叠加来显示特征提取的区域。研究结果有望促进CRC LNM检测中有效方法的使用，从而有助于设计适当的治疗计划。