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| title: PRETI Retinal Disease Detection | |
| emoji: π¬ | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 4.0.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # PRETI Retinal Disease Detection System | |
| **Undergraduate Thesis β Department of Biomedical Engineering β 2025** | |
| ## What this does | |
| This system detects 4 retinal diseases simultaneously from a single fundus photograph: | |
| - **DR** β Diabetic Retinopathy | |
| - **Glaucoma** | |
| - **HR** β Hypertensive Retinopathy | |
| - **RVO** β Retinal Vein Occlusion | |
| It also demonstrates **AGPT (Attention-Guided Patch Transmission)** β a novel mechanism that uses PRETI's RAAM attention maps to select only disease-relevant image patches for transmission, achieving **70.3% bandwidth reduction** for rural telemedicine. | |
| ## Results | |
| | Disease | AUC | | |
| |---|---| | |
| | DR | 0.9869 | | |
| | Glaucoma | 0.9999 | | |
| | HR | 0.9881 | | |
| | RVO | 0.9864 | | |
| | **Macro** | **0.9903** | | |
| ## References | |
| - PRETI: Lee et al., arXiv:2505.12233, 2025 | |
| - Focal Loss: Lin et al., IEEE TPAMI, 2020 | |
| - Class-Balanced Sampler: Cui et al., CVPR 2019 | |
| - ViT: Dosovitskiy et al., ICLR 2021 | |