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Dataset Card: GazeIntent = RadSeq & RadExplore & RadHybrid
Dataset Name: phamtrongthang/GazeIntent
Repository: UARK‑AICV/RadGazeIntent
License: CC BY-NC-SA 4.0
1. Dataset Summary
GazeIntent is the first intention-labeled eye-tracking dataset for radiological interpretation, capturing radiologist's diagnostic intentions during chest X-ray analysis. It includes:
- 3,562 chest X-ray samples with expert radiologist eye-tracking data
- Fine-grained intention labels for each fixation point
- Three distinct intention modeling paradigms representing different visual search behaviors
- Multi-label annotations for 13 radiological findings
This dataset supports research in intention interpretation, gaze-informed diagnosis, cognitive modeling, and explainable AI in medical imaging.
🏅 This work was accepted at ACM MM 2025 - A top-tier international conference on multimedia research.
2. Dataset Structure
| Attribute | Description |
|---|---|
| Total Samples | 3,562 chest X-rays |
| Sources | EGD (1,079) + REFLACX (2,483) |
| Modality | Chest X-ray images |
| Gaze Data | 2D coordinates + fixation duration + intention labels |
| Intention Classes | 13 radiological findings |
| Radiologists | Multiple expert radiologists |
3. Three Intention Paradigms
RadSeq (Systematic Sequential Search)
- Models radiologists following a structured diagnostic checklist
- One finding examined at a time in sequential order
- Reflects systematic, methodical visual search patterns
RadExplore (Uncertainty-driven Exploration)
- Captures opportunistic visual search behavior
- Radiologists consider multiple findings simultaneously
- Represents exploratory, uncertainty-driven attention
RadHybrid (Hybrid Pattern)
- Combines initial broad scanning with focused examination
- Two-phase approach: overview → targeted search
- Reflects real-world diagnostic behavior patterns
4. Intended Uses
- Radiologist intention interpretation and prediction
- Gaze-informed medical diagnosis systems
- Cognitive modeling of expert visual reasoning
- Medical education and training assessment
- Explainable AI for radiology applications
- Human-AI collaboration in medical imaging
5. Tasks and Benchmarks
Primary Task: Fixation-based Intention Classification
- Baseline: RadGazeIntent (transformer-based architecture)
- Input: Fixation sequences + chest X-ray images
- Output: Intention confidence scores for 13 findings
Evaluation Metrics:
- Classification: Accuracy, F1-score, Precision, Recall
- Multi-label: Per-class and macro-averaged metrics
Findings Covered: Atelectasis, Cardiomegaly, Consolidation, Edema, Enlarged Cardiomediastinum, Fracture, Lung Lesion, Lung Opacity, Pleural Effusion, Pleural Other, Pneumonia, Pneumothorax, Support Devices
6. Data Availability
The processed intention-labeled datasets are publicly available via Hugging Face under CC BY-NC-SA 4.0 license.
Access Requirements: Users must agree to share contact information and accept the license terms to access the dataset files.
7. Technical Details
Data Processing: Three datasets derived from existing eye-tracking sources (EGD, REFLACX) using different intention modeling assumptions:
- Uncertainty Filtering: Assigns labels based on temporal alignment with radiologist transcripts
- Sequential Constraints: Applies GazeSearch methodology for systematic search modeling
- Hybrid Integration: Combines initial scanning phase with focused examination periods
8. Citation
Please cite this dataset using the following BibTeX entry:
@article{pham2025interpreting,
title={Interpreting Radiologist's Intention from Eye Movements in Chest X-ray Diagnosis},
author={Pham, Trong-Thang and Nguyen, Anh and Deng, Zhigang and Wu, Carol C and Nguyen, Hien and Le, Ngan},
journal={arXiv preprint arXiv:2507.12461},
year={2025}
}
9. Acknowledgments
This work is supported by:
- National Science Foundation (NSF) Award No OIA-1946391, NSF 2223793 EFRI BRAID
- National Institutes of Health (NIH) 1R01CA277739-01
- Built upon EGD and REFLACX eye-tracking datasets
Contact: Trong Thang Pham (tp030@uark.edu)