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--- |
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datasets: |
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- EchoDynamic |
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- RVENet |
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- EchoNet-Pediatric-LVH |
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language: en |
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library_name: pytorch |
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license: mit |
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tags: |
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- self-supervised-learning |
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- echocardiography |
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- medical-imaging |
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- video-representation |
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model_index: deep-learning |
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paper: https://arxiv.org/pdf/2506.11777 |
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pipeline_tag: video-feature-extraction |
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--- |
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# 🫀 DISCOVR — Self-Supervised Echocardiography Representations |
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**Paper:** *Self-Supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation* — NeurIPS 2025 |
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📄 [arXiv:2506.11777](https://arxiv.org/pdf/2506.11777) |
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**Code:** [https://github.com/mdivyanshu97/DISCOVR](https://github.com/mdivyanshu97/DISCOVR) |
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--- |
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## 📦 Available Checkpoints |
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| Epochs | Filename | Description | |
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|:-------:|:-----------|:-------------| |
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| 200 | `checkpoint-199.pth` | Model trained for ~200 epochs | |
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| 300 | `checkpoint-299.pth` | Model trained for ~300 epochs | |
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| 400 | `checkpoint-399.pth` | Model trained for ~400 epochs | |
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| 600 | `checkpoint-599.pth` | Model trained for ~600 epochs | |
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| 800 | `checkpoint-799.pth` | Model trained for ~800 epochs | |
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> Each checkpoint corresponds to a model trained for the indicated number of epochs on **adult and pediatric echocardiography datasets** (EchoDynamic, RVENet, EchoNet-Pediatric LVH). |
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--- |
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## 🧠 Model Overview |
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DISCOVR is a self-supervised framework for learning spatio-temporal echocardiographic video representations via **online cluster distillation**. |
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It learns both fine-grained anatomical semantics and global temporal dynamics, supporting downstream tasks such as: |
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- Cardiac view classification |
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- Functional abnormality detection |
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- Video segmentation |
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- Representation learning for medical imaging |
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**Not for clinical or diagnostic use.** |
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--- |
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## Sample Usage |
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To pretrain the model on echocardiographic videos: |
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```bash |
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python -m torch.distributed.launch --nproc_per_node=NUM_GPUS \ |
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scripts/run_mae_pretraining.py \ |
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--data_path /path/to/echo_videos \ |
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--data_path_csv /path/to/train.csv \ |
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--data_path_val /path/to/val.csv \ |
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--data_path_test /path/to/test.csv \ |
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--mask_type multi_local \ |
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--loss_func SIGMA \ |
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--model pretrain_videomae_base_patch16_224 \ |
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--batch_size 48 \ |
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--num_frames 64 \ |
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--opt adamw \ |
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--opt_betas 0.9 0.95 \ |
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--warmup_epochs 40 \ |
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--epochs 400 |
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``` |
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--- |
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## 🔖 Quick Facts |
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- **Repo:** `Div97/DISCOVR_ADULT_PEDIATRIC_MODEL` |
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- **Model family:** DISCOVR checkpoints (199 → 799) |
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- **Architecture:** ViT-Base backbone, 64-frame clips (stride 3) |
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- **Datasets used:** EchoDynamic, RVENet, EchoNet-Pediatric LVH |
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- **Training objective:** Self-supervised online cluster distillation |
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- **Intended use:** Research & education |
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- **Not intended for:** Clinical decision-making or real-world patient care |
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--- |
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## 🧩 Citation |
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If you use DISCOVR in your work, please cite: |
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```bibtex |
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@article{mishra2025self, |
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title={Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation}, |
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author={Mishra, Divyanshu and Salehi, Mohammadreza and Saha, Pramit Saha and Patey, Olga and Papageorghiou, Aris T and Asano, Yuki M and Noble, J Alison}, |
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journal={arXiv preprint arXiv:2506.11777}, |
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year={2025} |
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} |
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``` |
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--- |
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## License |
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This project is licensed under the MIT License. |
