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Vulvovaginal candidiasis (VVC) is the most prevalent human candidalinfection, estimated to afflict approximately 75% of all women at least once intheir lifetime. It will lead to several symptoms including pruritus, vaginalsoreness, and so on. Automatic whole slide image (WSI) classification is highlydemanded, for the huge burden of disease control and prevention. However, theWSI-based computer-aided VCC screening method is still vacant due to the scarcelabeled data and unique properties of candida. Candida in WSI is challenging tobe captured by conventional classification models due to its distinctiveelongated shape, the small proportion of their spatial distribution, and thestyle gap from WSIs. To make the model focus on the candida easier, we proposean attention-guided method, which can obtain a robust diagnosis classificationmodel. Specifically, we first use a pre-trained detection model as priorinstruction to initialize the classification model. Then we design a SkipSelf-Attention module to refine the attention onto the fined-grained featuresof candida. Finally, we use a contrastive learning method to alleviate theoverfitting caused by the style gap of WSIs and suppress the attention to falsepositive regions. Our experimental results demonstrate that our frameworkachieves state-of-the-art performance. Code and example data are available athttps://github.com/cjdbehumble/MICCAI2023-VVC-Screening.

Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis Screening