An efficient framework based on large foundation model for cervical cytopathology whole slide image screening

TL;DR

This paper introduces an efficient weakly supervised framework leveraging large foundation models and contrastive learning for cervical cytopathology WSI classification, reducing annotation effort and improving performance.

Contribution

It proposes a novel strategy using pretrained foundation models with parameter-efficient fine-tuning and patch filtering to enhance WSI classification accuracy.

Findings

Achieved state-of-the-art results on CSD and FNAC 2019 datasets.

Enhanced MIL method performance with reduced computational resources.

Demonstrated effectiveness of unsupervised and weakly supervised learning in pathology.

Abstract

Current cervical cytopathology whole slide image (WSI) screening primarily relies on detection-based approaches, which are limited in performance due to the expense and time-consuming annotation process. Multiple Instance Learning (MIL), a weakly supervised approach that relies solely on bag-level labels, can effectively alleviate these challenges. Nonetheless, MIL commonly employs frozen pretrained models or self-supervised learning for feature extraction, which suffers from low efficacy or inefficiency. In this paper, we propose an efficient framework for cervical cytopathology WSI classification using only WSI-level labels through unsupervised and weakly supervised learning. Given the sparse and dispersed nature of abnormal cells within cytopathological WSIs, we propose a strategy that leverages the pretrained foundation model to filter the top$k$ high-risk patches. Subsequently, we suggest parameter-efficient fine-tuning (PEFT) of a large foundation model using contrastive learning on the filtered patches to enhance its representation ability for task-specific signals. By training only the added linear adapters, we enhance the learning of patch-level features with substantially reduced time and memory consumption. Experiments conducted on the CSD and FNAC 2019 datasets demonstrate that the proposed method enhances the performance of various MIL methods and achieves state-of-the-art (SOTA) performance. The code and trained models are publicly available at https://github.com/CVIU-CSU/TCT-InfoNCE.