| --- |
| datasets: |
| - HC18 |
| license: cc-by-nc-4.0 |
| metrics: |
| - f1 |
| - auroc |
| pipeline_tag: zero-shot-image-classification |
| tags: |
| - medical |
| - ultrasound |
| - vision |
| - knowledge-distillation |
| --- |
| |
| # MobileFetalCLIP |
|
|
| **Selective Repulsive Knowledge Distillation for Mobile Fetal Ultrasound Analysis** |
|
|
| [Project Website](https://numansaeed.com/MobileFetalCLIP/) | [Paper](https://huggingface.co/papers/2603.05421) | [GitHub Repository](https://github.com/numanai/MobileFetalCLIP) |
|
|
| MobileFetalCLIP is a highly efficient foundation model designed specifically for fetal ultrasound analysis on point-of-care, low-resource devices (like smartphones). It achieves this by distilling knowledge from a massive 427M parameter teacher model into a tiny 11.4M parameter student model using a novel technique called **Selective Repulsive Knowledge Distillation**. |
|
|
| Despite being **26× smaller** and **24× faster**, MobileFetalCLIP *surpasses* its massive teacher on standard validity benchmarks (HC18) and retains 97-98% of linear probing performance across tasks. |
|
|
| ## Model Details |
|
|
| - **Architecture:** FastViT (Student) distilled from ViT-L/14 (Teacher) |
| - **Parameters:** 11.4M Visual Parameters (75M Total) |
| - **Modality:** Ultrasound Image / Text |
| - **License:** CC BY-NC 4.0 (Non-Commercial Research Use Only) |
|
|
| ## Key Contributions |
|
|
| 1. **Selective Repulsive KD:** A novel methodology that explicitly pushes apart non-matching image-text embeddings during distillation, improving representation geometry. |
| 2. **Mobile Deployment:** Native efficiency, capable of running inference at 1.6ms on an iPhone 16 Pro (compared to the teacher which entirely OOMs). |
| 3. **SOTA Performance:** Establishes a new efficiency-accuracy Pareto frontier for prenatal ultrasound AI. |
|
|
| ## Usage |
|
|
| Please refer to the official GitHub repository for installation instructions, dataset preparation, and inference scripts: |
| 🔗 **[GitHub: numanai/MobileFetalCLIP](https://github.com/numanai/MobileFetalCLIP)** |
|
|
| ## Citation |
|
|
| If you find this model or codebase useful for your research, please cite the paper: |
|
|
| ```bibtex |
| @article{saeed2026mobilefetalclip, |
| title = {MobileFetalCLIP: Selective Repulsive Knowledge Distillation |
| for Mobile Fetal Ultrasound Analysis}, |
| author = {Saeed, Numan and Maani, Fadillah Adamsyah and Yaqub, Mohammad}, |
| journal = {arXiv preprint arXiv:2603.05421}, |
| year = {2026} |
| } |
| ``` |
