用眼动轨迹预测放射科医生经验水平，准确率超90%。
Predicting Radiologist Expertise from 3D Gaze Patterns During CT Interpretation
- 将眼动数据融入3D Transformer，通过注意力偏置和加权池化融合视觉行为
- 在182次阅片中达到0.91的ROC-AUC和0.86的F1分数
- 适合医学影像智能评估、人机协同诊断系统研究者参考
三维CT图像的准确解读需要高效导航体积数据并关注诊断相关区域。尽管眼动追踪在二维医学影像中被广泛研究，但在CT场景下的能力评估应用仍有限。本文提出一种基于眼动信息的Transformer框架，用于胸腔CT中的经验水平分类。采用DINOv2主干网络，通过可学习的对数空间注意力偏置和注视加权的图像块嵌入池化，将放射科医生的注视模式融入体积特征学习。在5位不同经验水平放射科医生的182次阅片会话上进行训练与评估，模型在保留测试集上取得0.91的ROC-AUC和0.86的F1分数，优于对比方法。结果表明，将视觉搜索行为纳入Transformer可支持客观、基于过程的放射科经验评估。代码已公开于https://github.com/leiluk1/GazeToSkill。
原文摘要 · Abstract (English)
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.
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