README.md

---
datasets:
- EchoDynamic
- RVENet
- EchoNet-Pediatric-LVH
language: en
library_name: pytorch
license: mit
tags:
- self-supervised-learning
- echocardiography
- medical-imaging
- video-representation
model_index: deep-learning
paper: https://arxiv.org/pdf/2506.11777
pipeline_tag: video-feature-extraction
---

# 🫀 DISCOVR — Self-Supervised Echocardiography Representations

**Paper:** *Self-Supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation* — NeurIPS 2025  
📄 [arXiv:2506.11777](https://arxiv.org/pdf/2506.11777)
**Code:** [https://github.com/mdivyanshu97/DISCOVR](https://github.com/mdivyanshu97/DISCOVR)

---

## 📦 Available Checkpoints

| Epochs | Filename | Description |
|:-------:|:-----------|:-------------|
| 200 | `checkpoint-199.pth` | Model trained for ~200 epochs |
| 300 | `checkpoint-299.pth` | Model trained for ~300 epochs |
| 400 | `checkpoint-399.pth` | Model trained for ~400 epochs |
| 600 | `checkpoint-599.pth` | Model trained for ~600 epochs |
| 800 | `checkpoint-799.pth` | Model trained for ~800 epochs |

> Each checkpoint corresponds to a model trained for the indicated number of epochs on **adult and pediatric echocardiography datasets** (EchoDynamic, RVENet, EchoNet-Pediatric LVH).

---

## 🧠 Model Overview

DISCOVR is a self-supervised framework for learning spatio-temporal echocardiographic video representations via **online cluster distillation**.  
It learns both fine-grained anatomical semantics and global temporal dynamics, supporting downstream tasks such as:
- Cardiac view classification  
- Functional abnormality detection  
- Video segmentation  
- Representation learning for medical imaging  

**Not for clinical or diagnostic use.**

---

## Sample Usage

To pretrain the model on echocardiographic videos:

```bash
python -m torch.distributed.launch --nproc_per_node=NUM_GPUS \
    scripts/run_mae_pretraining.py \
    --data_path /path/to/echo_videos \
    --data_path_csv /path/to/train.csv \
    --data_path_val /path/to/val.csv \
    --data_path_test /path/to/test.csv \
    --mask_type multi_local \
    --loss_func SIGMA \
    --model pretrain_videomae_base_patch16_224 \
    --batch_size 48 \
    --num_frames 64 \
    --opt adamw \
    --opt_betas 0.9 0.95 \
    --warmup_epochs 40 \
    --epochs 400
```

---

## 🔖 Quick Facts
- **Repo:** `Div97/DISCOVR_ADULT_PEDIATRIC_MODEL`  
- **Model family:** DISCOVR checkpoints (199 → 799)  
- **Architecture:** ViT-Base backbone, 64-frame clips (stride 3)  
- **Datasets used:** EchoDynamic, RVENet, EchoNet-Pediatric LVH  
- **Training objective:** Self-supervised online cluster distillation  
- **Intended use:** Research & education  
- **Not intended for:** Clinical decision-making or real-world patient care  

---

## 🧩 Citation

If you use DISCOVR in your work, please cite:

```bibtex
@article{mishra2025self,
  title={Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation},
  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},
  journal={arXiv preprint arXiv:2506.11777},
  year={2025}
}
```

---

## License
This project is licensed under the MIT License.