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.

翻译：外阴阴道假丝酵母菌病的精确检测对女性健康至关重要，但其稀疏分布及视觉模糊特征给病理学家和神经网络带来准确识别的重大挑战。我们的眼动追踪数据显示，专家经过深思熟虑后未标记、但持续关注的区域往往与神经网络的假阳性结果高度吻合。基于这一发现，我们提出Gaze-DETR这一创新方法，通过整合注视数据来减少假阳性以提升神经网络精度。Gaze-DETR包含适用于各类检测方法的通用注视引导热身协议，以及专为基于DETR模型设计的注视引导校正策略。综合实验证明，Gaze-DETR超越现有领先方法，在检测准确性和泛化能力上展现出显著提升。