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Predicting Radiologist Expertise from 3D Gaze Patterns During CT Interpretation
Book chapter

Predicting Radiologist Expertise from 3D Gaze Patterns During CT Interpretation

Leila Khaertdinova, Anna Anikina, Claudia Mello-Thoms and Bulat Ibragimov
Medical Image Computing and Computer Assisted Intervention – MICCAI 2026, pp.429-439
Lecture Notes in Computer Science, v. 16896, Springer Nature Switzerland
2026
DOI: 10.1007/978-3-032-38260-3_41

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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.
 Eye tracking Skill assessment Vision Transformer

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