Div97/DISCOVR_ADULT_PEDIATRIC_MODEL

🫀 DISCOVR — Self-Supervised Echocardiography Representations

Paper: Self-Supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation — NeurIPS 2025
📄 arXiv:2506.11777

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📦 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).

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🧠 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.

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🔖 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

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🧩 Citation

If you use DISCOVR in your work, please cite:

@article{mishra2025self,
  title={Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation},
  author={Mishra, Divyanshu and Salehi, Mohammadreza and Saha, Pramit and Patey, Olga and Papageorghiou, Aris T and Asano, Yuki M and Noble, J Alison},
  journal={arXiv preprint arXiv:2506.11777},
  year={2025}
}