Spaces:
Running on Zero
Running on Zero
| title: MoPET Medical Classification | |
| emoji: 🩺 | |
| colorFrom: gray | |
| colorTo: pink | |
| sdk: gradio | |
| sdk_version: 6.22.0 | |
| app_file: app.py | |
| short_description: MoPET mixture-of-experts medical image classification | |
| python_version: "3.12" | |
| startup_duration_timeout: 30m | |
| # MoPET Medical Classification | |
| This Space demos **MoPET: Parameter-Efficient Mixture-of-Experts for Unified | |
| Medical Image Classification** (EMA4MICCAI 2026 Workshop). | |
| MoPET adapts a *frozen* DINOv3 ViT-B/16 backbone with a learned sparse top-k | |
| router over a heterogeneous pool of LoRA + BOFT PEFT experts injected into the | |
| attention `qkv` projections. A single model consolidates four MedMNIST+ | |
| classification tasks (Blood, Breast, Derma, Path) behind one shared, | |
| sparsely-routed expert pool. | |
| ## Usage | |
| 1. Upload a medical image (blood cell microscopy, breast ultrasound, dermoscopy, | |
| or colon pathology histology). | |
| 2. Select the matching task head. | |
| 3. Click **Classify** to get per-class probabilities. | |
| > **Disclaimer:** This is a research artifact, not a diagnostic device. Outputs | |
| > must not be used for clinical diagnosis. | |
| ## Links | |
| - [Paper (arXiv)](https://arxiv.org/abs/2607.29462) | |
| - [GitHub](https://github.com/sdoerrich97/mopet) | |
| - [Pretrained Weights](https://huggingface.co/sdoerrich97/mopet_dinov3_unified_blood_breast_derma_path) |