{"slug": "predicting-radiologist-expertise-from-3d-gaze-patterns-during-ct-interpretation", "title": "Predicting Radiologist Expertise from 3D Gaze Patterns During CT Interpretation", "summary": "A new gaze-informed transformer framework predicts radiologist expertise from 3D gaze patterns during thoracic CT interpretation, achieving an ROC-AUC of 0.91 and F1 score of 0.86 on a held-out test set of 182 CT reading sessions from five radiologists. The model, developed by researchers and available on GitHub, integrates fixation patterns via a DINOv2 backbone with learnable log-space bias and gaze-weighted pooling, outperforming adapted methods and suggesting objective, process-based expertise assessment in radiology.", "body_md": "arXiv:2608.23836v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/predicting-radiologist-expertise-from-3d-gaze-patterns-during-ct-interpretation", "canonical_source": "https://arxiv.org/abs/2608.23836", "published_at": "2026-08-26 04:00:00+00:00", "updated_at": "2026-08-26 04:14:03.853546+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "computer-vision"], "entities": ["DINOv2", "GitHub"], "alternates": {"html": "https://wpnews.pro/news/predicting-radiologist-expertise-from-3d-gaze-patterns-during-ct-interpretation", "markdown": "https://wpnews.pro/news/predicting-radiologist-expertise-from-3d-gaze-patterns-during-ct-interpretation.md", "text": "https://wpnews.pro/news/predicting-radiologist-expertise-from-3d-gaze-patterns-during-ct-interpretation.txt", "jsonld": "https://wpnews.pro/news/predicting-radiologist-expertise-from-3d-gaze-patterns-during-ct-interpretation.jsonld"}}