构建专家眼动数据集，助力可解释癌症影像AI发展
GazeXPErT: An Expert Eye-tracking Dataset for Interpretable and Explainable AI in Oncologic FDG-PET/CT Scans
- 采集346例双阅片的4D眼动数据，标注肿瘤检测与测量决策窗口
- 眼动信号可提升模型性能（Dice达0.6819），辅助动态定位病灶
- 适合研究可解释AI、人机交互与医学影像诊断的开发者
[18F]FDG-PET/CT是指导肿瘤治疗的核心影像技术，但临床专家短缺亟需高效辅助工具。尽管已有自动病灶检测的独立AI模型，其临床应用仍受限于可解释性、可靠性及工作流整合。当前放射科人机交互主要依赖键盘、鼠标和语音，忽视了专家更快速自然的眼动信号。本文提出GazeXPErT，一个包含346例双阅片的4D眼动数据集，附带专家决策窗口标注，用于肿瘤检测与测量。数据集包含9,030条从3,948分钟60Hz眼动数据中提取的注视-病灶轨迹，以COCO格式呈现。该数据集捕捉专家在判断可疑病灶时的视觉推理模式，旨在通过理解专家眼动规律，推动可信、可解释、交互式AI模型的发展。基线实验表明，常规收集的眼动信号可提取显著特征（3D nnU-Net Dice: 0.6819 vs. 0.6008无眼动），眼动训练的视觉变换器有助于动态病灶定位（74.95%预测眼动更接近病灶），且专家意图可由原始眼动预测（准确率67.53%，AUROC 0.747）。
原文摘要 · Abstract (English)
[18F]FDG-PET/CT is a cornerstone imaging modality for guiding oncology therapies, yet human expert shortages necessitate more efficient diagnostic aids. While standalone AI models for automatic lesion detection exist, clinical translation remains hindered by AI explainability, reliability, and workflow integration. Meanwhile, human-computer-interaction in radiology remain limited to keyboard, mouse and voice, ignoring experts' faster, natural gaze signal. We present GazeXPErT, a 4D eye-tracking dataset with annotated expert decision windows for tumor detection and measurement on 346 dual-read FDG-PET/CTs. The dataset contributes 9,030 gaze-to-lesion trajectories derived from 3,948 minutes of 60 Hz eye-tracking data, rendered in COCO-style format. GazeXPErT captures experts' visual reasoning patterns when adjudicating suspicious lesions. It aims to facilitate development of trusted, explainable and interactive AI models through understanding expert gaze patterns. Baseline feasibility experiments suggest salient signal is extractable from routinely collected expert gaze (3D nnU-Net Dice: 0.6819 versus 0.6008 without), that gaze-trained vision transformers may aid dynamic lesion localization (74.95% predicted gaze closer to tumor), and that experts' intent may be predictable from raw gaze (Accuracy 67.53%, AUROC 0.747).
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