  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.
