Weakly supervised pathological whole slide image classification based on contrastive learning

In the context of dealing with limited annotated data, this paper introduces a weakly supervised whole slide image (WSI) classification approach based on contrastive learning. The proposed method aims to detect whether cancer cells have metastasized in anterior lymph nodes of breast cancer in whole slide images. Initially, small patches are extracted from whole-slide pathology images, and an unsupervised pretraining is performed on the feature extraction model using the MoCo v2 framework. Subsequently, the feature extraction model is used to extract features from the small patches. Finally, CLAM is employed to aggregate the extracted features to obtain the overall whole slide image (WSI) classification results. Experimental results demonstrate that using MoCo v2 for unsupervised pretraining of the feature extraction model achieves an accuracy of 0.8808 in the small patch classification task. Moreover, under coarse-grained WSI-level labels, the proposed approach achieves area under the receiver operating characteristic curve (AUC) values of 0.957 +/- 0.0276 and 0.9442 on different datasets, outperforming typical weakly supervised and partially supervised methods in terms of classification performance.