LungHistoNet is a deep learning project that utilizes Vision Transformers to analyze lung histology images. The goal is to enhance the detection and classification of pulmonary diseases through advanced AI techniques.
- Fix/optimize the real-time plot request in the application.
- Create an account at Docker Hub and set up a password.
- Download Docker from Docker.
- If using an M1/M2 Mac, select the Apple Core version.
- If using an Intel Mac, select the Intel Core version.
- Install the application.
- Open Terminal.
- Login to Docker using your Docker Hub credentials:
Follow the instructions to log in (ensure you verify your email to log in successfully).
docker login
- Pull the application by running the following command:
docker pull amirhosseinebrahimi/lung-injury:latset_v1.6
- Check if Brew is installed. If not, open Terminal and run:
/bin/bash -c "$(curl –fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" - Set up Brew by running the following commands (replace
XXXwith your laptop username):echo >> /Users/XXX/.zprofile echo 'eval "$(/opt/homebrew/bin/brew shellenv)"' >> /Users/XXX/.zprofile eval "$(/opt/homebrew/bin/brew shellenv)"
- Install
socatusing Brew:brew install socat
- Open Terminal and set up the display (leave the Terminal open after pressing Enter):
- MacOS
socat TCP-LISTEN:6000,reuseaddr,fork UNIX-CLIENT:"$DISPLAY" - Linux (debian)
sudo xhost +local:docker
- Open another Terminal and run the following command (replace
XXXwith your laptop username):
-
MacOS
docker run -it --rm -e DISPLAY=host.docker.internal:0 \ amirhosseinebrahimi/lung-injury:latest_v1.6
-
Linux (debian)
sudo docker run -it --rm -e DISPLAY=$DISPLAY \ -v /tmp/.X11-unix:/tmp/.X11-unix \ amirhosseinebrahimi/lung-injury:latest_v1.6