| --- |
| license: other |
| task_categories: |
| - image-classification |
| language: |
| - en |
| - zh |
| tags: |
| - fundus |
| - ophthalmology |
| - retinal-imaging |
| - medical-image-classification |
| - retfound |
| - resnet |
| - vit |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # GPT-Image Fundus Benchmark |
|
|
| This repository contains a processed fundus-image benchmark, training/evaluation code, experiment outputs, and model checkpoints for four ophthalmic classification directions: |
|
|
| - **Myopia / Myopic Maculopathy**: MMAC, 5 classes |
| - **AMD**: ADAM, binary classification |
| - **Glaucoma**: AIROGS and PAPILA, binary classification |
| - **Diabetic Retinopathy (DR)**: IDRiD, APTOS-2019, DeepDRiD, 5 classes |
|
|
| The repository was prepared for experiments comparing **RetFound**, **ResNet-50**, and **ViT-B/16**, including full-data baselines and data-scarcity downsampling experiments. |
|
|
| ## Repository Layout |
|
|
| ```text |
| code/ Training, preprocessing, evaluation, and report scripts |
| data/ Processed ImageFolder datasets: train/val/test/<label>/ |
| results/ Metrics, plots, logs, predictions, HTML report outputs (no checkpoint files here) |
| weights/pretrained/ RETFound pretrained CFP/OCT weights |
| weights/checkpoints/baseline/ Best checkpoints for 7 datasets x 3 models |
| weights/checkpoints/downsample/ Best checkpoints for ADAM/AIROGS/PAPILA downsampling experiments |
| weights/checkpoints/legacy_retfound/ Old-format RetFound checkpoints kept for full provenance |
| metadata/ Sample packs, quality-label spreadsheets, and report artifacts |
| weights_manifest.csv Manifest of every uploaded weight file |
| FILES.md Directory-level file inventory |
| ``` |
|
|
| ## Data Format |
|
|
| Each dataset under `data/` follows a standard ImageFolder layout: |
|
|
| ```text |
| data/<Disease>/<Dataset>/ |
| train/<label>/*.jpg |
| val/<label>/*.jpg |
| test/<label>/*.jpg |
| labels.csv |
| ``` |
|
|
| `labels.csv` contains: |
|
|
| ```text |
| split, filepath, label, class_name, orig_id |
| ``` |
|
|
| ## Model Checkpoints |
|
|
| All weights are centralized under `weights/` for easier reuse on another server. |
|
|
| - `weights/pretrained/RETFound_mae_natureCFP.pth`: RETFound CFP pretrained checkpoint used for fundus-image fine-tuning. |
| - `weights/pretrained/RETFound_mae_natureOCT.pth`: RETFound OCT pretrained checkpoint retained for completeness. |
| - `weights/checkpoints/baseline/`: best checkpoints from the full-data benchmark. |
| - `weights/checkpoints/downsample/`: best checkpoints from the data-scarcity experiments. |
| - `weights/checkpoints/legacy_retfound/`: old-format RetFound outputs retained for provenance; final metrics use the canonical `downsample/<dataset>/<pct>/retfound/` paths. |
|
|
| See `weights_manifest.csv` for the full checkpoint list. |
|
|
| ## Main Results |
|
|
| The benchmark results are summarized in: |
|
|
| - `metadata/report.html` and `results/report.html`: single-file HTML report with dataset backgrounds, class distributions, metrics, confusion matrices, ROC curves, per-class metrics, and data-scarcity analyses. |
| - `results/summary.csv`: full-data benchmark summary. |
| - `results/downsample/summary.csv`: downsampling experiment summary. |
|
|
| ## Reproducing / Running |
|
|
| The scripts in `code/` expect the project root to contain `data/`, `weights/`, and `results/` as laid out in this repository. |
|
|
| Typical entry points: |
|
|
| ```bash |
| python code/run_all.py --dry_run |
| python code/run_all.py |
| python code/downsample_experiment.py --dry_run |
| python code/downsample_experiment.py |
| python code/make_report.py |
| ``` |
|
|
| The environment used during experiments was a conda environment with PyTorch 2.5.1 + CUDA 12.1, timm, scikit-learn, pandas, matplotlib, seaborn, and openpyxl. |
|
|
| ## Data Source and License Notice |
|
|
| This repository aggregates and reformats data derived from multiple public ophthalmology datasets/challenges, including Kaggle, Zenodo, and challenge-hosted sources. Each original dataset may have its own license, terms of use, and citation requirements. Users are responsible for checking and complying with the original licenses before redistribution, publication, or commercial use. |
|
|
| This repository is intended for research and reproducibility. It is **not** intended for clinical diagnosis. |
|
|
| ## Quality Labels |
|
|
| Manual image-quality labels for ADAM and MMAC preview subsets are included in `metadata/`: |
|
|
| - `quality_review_AMD_ADAM_MMAC.rechecked_D_standard.csv` |
| - `图像质量类型明细_按数据集分sheet_追加标签.xlsx` |
|
|
| Quality categories include light haze, severe haze, low contrast, small-pupil/off-axis appearance, vignetting/peripheral edge-quality decline, reflection/glare, and occlusion. |
|
|
