Accurate detection of vulvovaginal candidiasis is critical for women's
health, yet its sparse distribution and visually ambiguous characteristics pose
significant challenges for accurate identification by pathologists and neural
networks alike. Our eye-tracking data reveals that areas garnering sustained
attention - yet not marked by experts after deliberation - are often aligned
with false positives of neural networks. Leveraging this finding, we introduce
Gaze-DETR, a pioneering method that integrates gaze data to enhance neural
network precision by diminishing false positives. Gaze-DETR incorporates a
universal gaze-guided warm-up protocol applicable across various detection
methods and a gaze-guided rectification strategy specifically designed for
DETR-based models. Our comprehensive tests confirm that Gaze-DETR surpasses
existing leading methods, showcasing remarkable improvements in detection
accuracy and generalizability.

准确检测阴道白色念珠菌病对女性健康至关重要，但其稀疏分布和视觉上模糊的特征对病理学家和神经网络的准确识别都带来了重大挑战。通过我们的眼球跟踪数据，我们发现吸引持续注意力的区域往往与神经网络的假阳性结果一致。基于该发现，我们引入了 Gaze-DETR，一种创新方法，通过减少假阳性来提高神经网络的精确度，该方法融入了适用于各种检测方法的通用注视引导热身协议和专为基于 DETR 模型设计的注视引导修正策略。我们的综合测试确认，Gaze-DETR 超越了现有的领先方法，在检测准确性和泛化能力方面展示出显著改进。

利用专家的凝视来减少阴道念珠菌感染筛查中的误判

Gaze-DETR: Using Expert Gaze to Reduce False Positives in Vulvovaginal  Candidiasis Screening

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
this https URL

提出了一种基于注意力引导的方法，利用全幅切片图像分类模型对阴道念珠菌进行诊断分类，通过细粒度特征的自注意模块和对虚假阳性区域的注意力抑制方法，有效地解决了 WSI 图像样式差异并取得了最先进的性能。