采用细胞形态特征对比学习的肺癌图像分类  

Lung Cancer Image Classification Using Cell Morphological Feature Contrastive Learning

作　　者：贾伟 江海峰[2] 赵雪芬[3] JIA Wei;JIANG Haifeng;ZHAO Xuefen(School of Information Engineering,Ningxia University,Yinchuan 750021,China;Department of Pathology,General Hospital of Ningxia Medical University,Yinchuan 750021,China;Xinhua College,Ningxia University,Yinchuan 750021,China)

机构地区：[1]宁夏大学信息工程学院,银川750021 [2]宁夏医科大学总医院病理科,银川750021 [3]宁夏大学新华学院,银川750021

出　　处：《计算机工程与应用》2023年第19期140-150,共11页Computer Engineering and Applications

基　　金：国家自然科学基金(62062057);宁夏自然科学基金(2020AAC03032)。

摘　　要：针对肺癌图像分类中出现的已标记肺癌病理图像较少且细胞形态复杂的问题,提出一种基于细胞形态特征对比学习的肺癌病理图像分类方法,通过对比学习将置信度较高的未标记数据混入到训练数据中,解决已标记数据不足的问题。在最近邻对比学习的基础上,提出基于最远和最邻近的对比学习,将最远和最近邻图像同时用于对比学习,通过增加对比样本的正样本学习难度和数据的多样性,提升对比学习的性能。将基于可变形卷积和动态卷积的ResNet50作为编码器,增强对细胞形态特征的提取能力。实验结果表明,在已标记数据较少的情况下,与现有的分类方法相比,该分类方法能够充分利用已标记和未标记癌症病理图像中的细胞特征信息,获得较好的分类效果。Aiming at the problems that the labeled pathological images of lung cancer are few and the cell morphology is complex in lung cancer image classification,a lung cancer pathological image classification method based on cell morpho-logical feature contrastive learning is proposed.Through contrastive learning,the unlabeled data with high confidence is mixed into the training data to solve the problem of insufficient labeled data.Based on the nearest-neighbor contrastive learning,a farthest and nearest-neighbors-based contrastive learning is proposed.The farthest and nearest neighbor images are used for the contrastive learning at the same time.The performance of the contrastive learning is improved by increasing the learning difficulty of the positive samples and the diversity of the data.Deformable convolution and dynamic convolution-based ResNet50 are used as an encoder to enhance the extraction ability of cell morphological features.The experimental results show that,in the case of less labeled data,compared with the existing classification methods,the proposed method can make full use of the cell feature information in the labeled and unlabeled cancer pathology images and obtain better classification results.

关 键 词：肺癌 病理图像分类 对比学习 可变形卷积 动态卷积 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]