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.

通过无监督学习和弱监督学习，提出一种仅利用整个宫颈细胞病理切片图像标签的高效框架，利用预训练模型进行筛选高风险区域，然后使用对比学习进行参数高效微调，通过仅训练添加的线性适配器，大大提高学习效果并实现了最先进的性能。

基于大型基础模型的宫颈细胞病理切片图像筛查高效框架