A semi-supervised learning approach with consistency regularization for tumor histopathological images analysis - Jiang, Yanyun; Sui, Xiaodan; Ding, Yanhui; Xiao, Wei; Zheng, Yuanjie; Zhang, Yongxin (Frontiers in Oncology, 2023)
Manual inspection of histopathological images is important in clinical cancer diagnosis. Pathologists implement pathological diagnosis and prognostic evaluation through the microscopic examination of histopathological slices. This entire process is time-consuming, laborious, and challenging for pathologists. The modern use of whole-slide imaging, which scans histopathology slides to digital slices, and analysis using computer-aided diagnosis is an essential problem. To solve the problem of difficult labeling of histopathological data, and improve the flexibility of histopathological analysis in clinical applications, we herein propose a semi-supervised learning algorithm coupled with consistency regularization strategy, called "Semi-supervised Histopathology Analysis Network" (Semi-His-Net), for automated normal-versus-tumor and subtype classifications. Specifically, when inputted disturbing versions of the same image, the model should predict similar outputs. Based on this, the model itself can assign artificial labels to unlabeled data for subsequent model training, thereby effectively reducing the labeled data required for training. Our Semi-His-Net is able to classify patches from breast cancer histopathological images into normal tissue and other three different tumor subtypes, and then are used to analyze whole-slide images. The algorithm is observed to be effective for other cancer types and is used to distinguish between normal and tumor areas.