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# Dataset Card: GazeIntent = RadSeq & RadExplore & RadHybrid

**Dataset Name**: `phamtrongthang/GazeIntent`  
**Repository**: [UARK‑AICV/RadGazeIntent](https://github.com/UARK-AICV/RadGazeIntent)  
**License**: CC BY-NC-SA 4.0

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## 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.

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## 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 |

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## 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

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## 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

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## 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

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## 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.

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## 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



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## 8. Citation

Please cite this dataset using the following BibTeX entry:

```bibtex
@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}
}
```

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## 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)