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.

翻译：外阴阴道念珠菌病（VVC）是最常见的人类念珠菌感染，据估计约75%的女性一生中至少会经历一次。该疾病会导致瘙痒、阴道疼痛等多种症状。由于疾病防控的巨大负担，全切片图像（WSI）的自动分类需求迫切。然而，基于WSI的计算机辅助VVC筛查方法仍属空白，其原因是标注数据稀缺以及念珠菌的特殊性质。念珠菌在WSI中具有独特的长形形态、空间分布占比极小以及WSI域间风格差异等特点，传统分类模型难以有效捕获。为使模型更易聚焦于念珠菌，我们提出了一种注意力引导方法，可构建鲁棒的诊断分类模型。具体而言，首先利用预训练的检测模型作为先验指导来初始化分类模型；其次设计跳跃自注意力模块，将注意力细化至念珠菌的细粒度特征；最后采用对比学习方法缓解因WSI风格差异导致的过拟合，并抑制对假阳性区域的关注。实验结果表明，我们的框架取得了最先进的性能。代码与示例数据已开源至https://github.com/cjdbehumble/MICCAI2023-VVC-Screening。