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

arXiv: https://arxiv.org/abs/2405.09463 · HTML: https://mlsanity.com/paper/2405.09463/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 15 May 2024.

## Builds on (most cited first, 17 of 17)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://mlsanity.com/paper/2011.12450/) (2020)
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022)
- [Conditional DETR for Fast Training Convergence](https://mlsanity.com/paper/2108.06152/) (2021)
- [Follow My Eye: Using Gaze to Supervise Computer-Aided Diagnosis](https://mlsanity.com/paper/2204.02976/) (2022)
- [GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification](https://mlsanity.com/paper/2305.18221/) (2023)
- [Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning](https://mlsanity.com/paper/2205.12466/) (2022)
- [GazeSAM: What You See is What You Segment](https://mlsanity.com/paper/2304.13844/) (2023)
- [Mining Gaze for Contrastive Learning toward Computer-Assisted Diagnosis](https://mlsanity.com/paper/2312.06069/) (2023)
- [EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography](https://mlsanity.com/paper/2109.12474/) (2021)
- [Learning Better Contrastive View from Radiologist's Gaze](https://mlsanity.com/paper/2305.08826/) (2023)
- [Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis Screening](https://mlsanity.com/paper/2309.02670/) (2023)

## Built on by (most cited first, 3 of 3)

- [Reading Recognition in the Wild](https://mlsanity.com/paper/2505.24848/) (2025)
- [Enjoying Information Dividend: Gaze Track-based Medical Weakly Supervised Segmentation](https://mlsanity.com/paper/2505.22230/) (2025)
- [From Clinical Intent to Clinical Model: Autonomous Coding-Agents for Clinician-driven AI Development](https://mlsanity.com/paper/2604.17110/) (2026)

## Abstract (arXiv)

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.