A newer version of the Gradio SDK is available: 6.15.2
metadata
title: DRIL OCT Classification
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 5.29.0
python_version: 3.11
app_file: app.py
pinned: false
license: mit
DRIL OCT Classification Demo
This Space provides an interactive demo for Diabetic Retinopathy-related Inner Layer (DRIL) detection from OCT B-scan images using fine-tuned deep learning models trained in the DRIL Classification Benchmark.
What is DRIL?
DRIL (Disruption of Retinal Inner Layers) refers to the loss of distinction between the inner plexiform, inner nuclear, and outer plexiform layers on OCT imaging. It is a recognized prognostic biomarker in diabetic macular edema associated with poor visual outcomes after treatment.
Models Available
| Model | Architecture | AUC (CV) |
|---|---|---|
| RETFound (Moderate FT) | Vision Transformer | Reported in paper |
| RETFound (Conservative FT) | Vision Transformer | Reported in paper |
| DenseNet-121 | CNN | Reported in paper |
| EfficientNet-B0 | CNN | Reported in paper |
How to Use
- Upload a fovea-centered OCT B-scan image (JPEG or PNG).
- Select the model you wish to use.
- Click "Classify" to get the DRIL probability and predicted label.
Notes
- The model was trained on a private OCT dataset (429 DRIL, 394 No-DRIL cases).
- Input images should be macular OCT B-scans. The model performs best on images similar to the training distribution.
- Test-time augmentation (TTA) is applied for more robust predictions.
- This demo is for research purposes only and should not be used for clinical decision-making.