---
license: cc-by-4.0
task_categories:
  - image-classification
  - object-detection
tags:
  - eye-tracking
  - gaze
  - medical-imaging
  - radiology
  - chest-imaging
  - saliency
  - ct-scan
  - x-ray
size_categories:
  - 1K<n<10K
---

# EyeAssist: Radiologist Eye-Tracking Datasets and Evaluation Protocols

EyeAssist is a collection of two multimodal medical imaging datasets paired with radiologist eye-tracking (gaze) data, together with reference evaluation code that demonstrates how the datasets are used in benchmarking studies.

The release contains two datasets:

- **EyeAssist-PE** — chest CT volumes for lung-cancer prognosis, paired with gaze recordings from 7 radiologists across 2 reading sessions.
- **EyeAssist-Neo** — neonatal chest X-rays paired with gaze recordings under multiple experimental conditions (expert vs. generalist; with vs. without clinical context).

Both are designed to support research on gaze-guided deep learning, explainability in medical AI, and modeling of radiologist visual behavior.

## Repository layout

```
EyeAssist/
├── Dataset/
│   ├── EyeAssist-PE/                  # chest CT + gaze (~4.9 GB)
│   │   ├── CT/                        # 40 CT volumes (NIfTI)
│   │   ├── Gaze/                      # gaze recordings, 7 readers × 2 sessions
│   │   ├── Saliency/                  # gaze-derived saliency maps + figures
│   │   ├── Clinical context.docx
│   │   ├── Expert Prognosis Decision.xlsx
│   │   └── README.md
│   └── EyeAssist-Neo/                 # neonatal X-ray + gaze (~462 MB)
│       ├── Xrays/                     # X-ray images (JPEG)
│       ├── Gaze&Saliency/             # gaze recordings under different conditions
│       └── clinical context.csv
└── Evaluation Protocol Code/
    ├── protocol1/                     # tabular feature baselines (per dataset)
    ├── protocol2/                     # deep saliency / transfer experiments
    └── protocol3/                     # gaze-weighted feature pooling (PE)
```

Each subdirectory contains its own README with dataset-specific schema and column definitions.

## Datasets at a glance

| | EyeAssist-PE | EyeAssist-Neo |
|---|---|---|
| Modality | Chest CT (NIfTI) | Chest X-ray (JPEG) |
| Cases | 40 (20 survival / 20 death; 20 central / 20 peripheral) | 100+ |
| Readers | 7 radiologists (R1–R7) | Experts, generalists, residents |
| Sessions | 2 reading sessions | Session 2 with multiple conditions |
| Conditions | Blind / Context | Expert vs Generalist; With vs Without Clinical Context |
| Gaze format | per-frame CSV (`Trial.csv`) | per-frame CSV (`fixations.csv`) |
| Labels | Survival outcome + expert prognosis | Diagnosis, gestational age, clinical context |

## Quick start

```python
import nibabel as nib
import pandas as pd

# EyeAssist-PE: load a CT volume + radiologist gaze
ct = nib.load("Dataset/EyeAssist-PE/CT/ca_42_1_diecentral.nii.gz").get_fdata()
gaze = pd.read_csv("Dataset/EyeAssist-PE/Gaze/Session 1/R1/Trial.csv")

# EyeAssist-Neo: load X-ray fixations
fix = pd.read_csv(
    "Dataset/EyeAssist-Neo/Gaze&Saliency/Session2 expert vs generalist/Expert/expert1/csv/fixations.csv"
)
```

## Evaluation Protocols

Three reference protocols are included under `Evaluation Protocol Code/`:

- **Protocol 1** — tabular gaze-feature baselines: extracts summary gaze statistics (fixation count, dwell time, entropy, ROI revisit rate, etc.) and trains classical classifiers. Runs on both datasets.
- **Protocol 2** — deep learning experiments: saliency-map prediction with the EML-NET backbone (vendored under `protocol2/EyeAssist-PE/models/EML-NET-Saliency/`), and transfer-learning experiments on the Neo dataset.
- **Protocol 3** — gaze-weighted feature pooling for PE: extracts CT features with U-Net / nnU-Net / SwinUNETR backbones and pools them by per-reader gaze density, comparing `no_gaze` / `blind` / `context` conditions.

Each protocol directory has its own README with run instructions and config defaults.

### Setup

```bash
pip install numpy pandas nibabel scipy scikit-learn scikit-image matplotlib torch torchvision pillow monai
```

Backbone-specific models (Models Genesis pretrained weights, MONAI SwinUNETR weights) are not bundled — see `Evaluation Protocol Code/protocol3/main.py` for the expected paths and download links.

## File formats

| Type | Format | Tools |
|---|---|---|
| CT volumes | `.nii`, `.nii.gz` | `nibabel`, `SimpleITK` |
| X-ray images | `.jpg`, `.jpeg` | `PIL`, `cv2` |
| Gaze data | `.csv` | `pandas` |
| Clinical context | `.csv`, `.docx` | `pandas`, `python-docx` |
| Prognosis labels | `.xlsx` | `pandas` (`openpyxl`) |
| Saliency maps | `.png`, `.npy` | `PIL`, `numpy` |
| Pretrained weights | `.pt`, `.pth` | `torch` |

## Reader anonymization

All radiologists, experts, and readers are referred to by anonymous identifiers (e.g. `R1`–`R7`, `expert1`–`expert5`, `generalist1`–`generalist5`, `reader1`–`reader3`). No personally identifying information is included.

## License

Released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). See [LICENSE](LICENSE) for details.

## Acknowledgements

The Protocol 2 saliency experiments use a vendored copy of the [EML-NET-Saliency](https://github.com/SenJia/EML-NET-Saliency) codebase by Sen Jia and Neil D. B. Bruce; see the upstream repository for license and citation.