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EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation

This repository contains the official inference code for the following paper:

EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation
Milos Vukadinovic, Xiu Tang, Neal Yuan, Paul Cheng, Debiao Li, Susan Cheng, Bryan He*, David Ouyang*
Read the paper on arXiv, See the demo

EchoPrime Demo

How To Use

  1. Clone the repository and navigate to the EchoPrime directory git clone https://github.com/echonet/EchoPrime
  2. Download model data
    • wget https://github.com/echonet/EchoPrime/releases/download/v1.0.0/model_data.zip
    • wget https://github.com/echonet/EchoPrime/releases/download/v1.0.0/candidate_embeddings_p1.pt
    • wget https://github.com/echonet/EchoPrime/releases/download/v1.0.0/candidate_embeddings_p2.pt
    • unzip model_data.zip
    • mv candidate_embeddings_p1.pt model_data/candidates_data/
    • mv candidate_embeddings_p2.pt model_data/candidates_data/
  3. Install requirements pip install -r requirements.txt
  4. Test on a sample input. 50 - number of videos, 3 number of channels, 16 - number of frames, 224 - height and width
from echo_prime import EchoPrime
import torch
ep = EchoPrime()
ep.predict_metrics(ep.encode_study(torch.zeros((50, 3, 16, 224, 224))))
  1. Follow EchoPrimeDemo.ipynb notebook to see how to correctly process the input and inference Echoprime.

Licence

This project is licensed under the terms of the MIT license.

FAQ:

How to load pretrained video encoder and text encoder for fune-tuning?

load_for_finetuning.py script shows how to load pretrained EchoPrime video and text encoder.

After processing the images they appear green-tinted.

Make sure that you have the correct libraries installed. Use requirements.txt to install the dependencies.

How to run the code in docker?

docker build -t echo-prime .
docker run -d --name echoprime-container --gpus all echo-prime tail -f /dev/null

Then you can attach to this container and run the notebook located at /workspace/EchoPrime/EchoPrimeDemo.ipynb.

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