Cervical Cytology Classification with Coarse Labels Based on Two-Stage Weakly Supervised Contrastive Learning Framework

Deep learning methods have achieved remarkable success in various tasks from cervical cytology images. However, for the gigapixel whole slide images (WSIs), the acquisition of annotations is a time-consuming and labor-intensive task requiring a high level of expertise. While the sparse distribution of malignant cells and the factors above pose great difficulties to label thousands of patches divided from the WSI, it is much easier to obtain the coarse labels at the WSI level. In this paper, we propose a novel weakly supervised contrastive learning framework, which utilizes only coarse labels from the WSIs for cervical cytology patch classification. The proposed framework consists of two stages, including the representation learning stage and the classifier finetuning stage. In the first stage, to effectively exploit useful information of coarse labels, we devise a re-weight cross-entropy loss, which can fast warm up the training and reduce the inexact supervision from the coarse labels simultaneously. To further excavate features bypassing the coarse labels, we propose a self-supervised contrastive loss, where the random augmentation and the mean teacher architecture enrich the external variations, and help better extract representations through patch similarities. In the second stage, based on ensemble predictions and uncertainty selections, reliable pseudo labels are generated for the inaccurate labels to finetune the classifier, with better performance achieved. Extensive experiments on the in-house dataset demonstrate that the proposed method is more efficient than other state-of-the-art methods. Our code is available on https://github.com/chaisiyii/WSCL.