Problem
Research questions and friction points this paper is trying to address.
3D Gaze Patterns
CT Interpretation
Expertise Assessment
Radiologist
Predicting Radiologist Expertise from 3D Gaze Patterns During CT Interpretation
🤖 AI Summary
研究通过在3D CT图像解读中结合放射科医生的注视模式和基于DINOv2的transformer框架，有效评估放射科医生的专业水平。
📝 Abstract
Accurate interpretation of volumetric CT requires efficient navigation of 3D image volumes and attention to diagnostically relevant regions. While eye-tracking has been widely studied in 2D medical imaging, its use for expertise assessment in CT settings remains limited. We propose a gaze-informed transformer framework for expertise classification in thoracic CT. Using a DINOv2 backbone, radiologist fixation patterns are integrated into volumetric feature learning through (1) a learnable log-space bias in self-attention and (2) gaze-weighted pooling of patch embeddings. We trained and evaluated our approach on 182 CT reading sessions from five radiologists with varying levels of experience. On a held-out test set, the model achieves an ROC-AUC of 0.91 and F1 score of 0.86, outperforming adapted methods. These findings suggest that incorporating visual search behavior into transformers may support objective, process-based expertise assessment in radiology. Code is available via https://github.com/leiluk1/GazeToSkill.
Innovation
Methods, ideas, or system contributions that make the work stand out.
gaze-informed transformer
DINOv2 backbone
self-attention with learnable log-space bias
gaze-weighted pooling
expertise classification
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