| --- |
| 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. |
|
|
