VNOpenAI / vn-aidr

VN AIDr - Open source medical image processing platform

VN AIDr - Open source medical image processing project

Project documentation (only Vietnamese): https://vnopenai.github.io/ai-doctor/.

This project contains baseline models for medical image processing and a web user interface for interacting with the system. Our goal is to create an open source code base for students, hobbylists, engineers, or even researchers to get familar with image processing, machine learning and deep learning through medical image processing problems. In this system, we also integrate natural language processing (NLP) models for automatic completion of medical reports.

Want to join us in this project? Send us a message via our contact form.

Click following image to play demo video.

Requirements

• Python 3.6 + Pip
• Detectron2: https://detectron2.readthedocs.io/en/latest/tutorials/install.html
• NodeJS + Yarn for frontend development

1. Server

• Download pretrained models here and extract them into trained_models.

• Install requirements:

pip install -r requirements.txt

Or for requirements for CUDA support (require CUDA 10.2):

pip install -r requirements-gpu.txt

• Update USE_GPU in configs/common.py to True to enable GPU support.

• Run the server:

python app.py --port 8080

• Open http://localhost:8080 in your browser to access web UI.

2. Frontend (UI)

Development

cd frontend
yarn
yarn start

Build

cd frontend
yarn
yarn build

3. Deployment using Docker

Without GPU support

• Build image

docker build . -t vnaidr

• Run server

docker run -it -p 8080:8080 vnaidr

• Open http://localhost:8080 in your browser to access web UI.

With GPU support (Only NVIDIA GPU with CUDA)

• Update USE_GPU in configs/common.py to True to enable GPU support.

• Build image

docker build -t vnaidr-gpu -f Dockerfile-gpu .

• Run server

docker run --gpus all -it  -p 8080:8080  vnaidr-gpu

• Open http://localhost:8080 in your browser to access web UI.

Copyright and License

The template was developed based on SB Admin 2.