Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis Screening

TL;DR

This paper introduces a novel attention-guided framework for automatic whole slide image classification to detect vulvovaginal candidiasis, addressing challenges like scarce labeled data and the unique shape of candida.

Contribution

It proposes a combined approach using a pre-trained detection model, a Skip Self-Attention module, and contrastive learning to improve diagnosis accuracy in WSI-based VVC screening.

Findings

Achieves state-of-the-art performance on VVC detection

Effectively captures candida features despite limited data

Reduces false positives through contrastive learning

Abstract

Vulvovaginal candidiasis (VVC) is the most prevalent human candidal infection, estimated to afflict approximately 75% of all women at least once in their lifetime. It will lead to several symptoms including pruritus, vaginal soreness, and so on. Automatic whole slide image (WSI) classification is highly demanded, for the huge burden of disease control and prevention. However, the WSI-based computer-aided VCC screening method is still vacant due to the scarce labeled data and unique properties of candida. Candida in WSI is challenging to be captured by conventional classification models due to its distinctive elongated shape, the small proportion of their spatial distribution, and the style gap from WSIs. To make the model focus on the candida easier, we propose an attention-guided method, which can obtain a robust diagnosis classification model. Specifically, we first use a pre-trained detection model as prior instruction to initialize the classification model. Then we design a Skip Self-Attention module to refine the attention onto the fined-grained features of candida. Finally, we use a contrastive learning method to alleviate the overfitting caused by the style gap of WSIs and suppress the attention to false positive regions. Our experimental results demonstrate that our framework achieves state-of-the-art performance. Code and example data are available at https://github.com/cjdbehumble/MICCAI2023-VVC-Screening.