mayushibata/rcc repository overview

Container for a Python web app predicting histological grade of clear cell renal cell carcinoma (ccRCC) using regression CNN models, as reported in Shibata et al. (2025) (https://doi.org/10.1016/j.reth.2025.01.011⁠).

Instructions for running containers in a local environment:

1. Pull the image on the target machine. Note: Use the "latest" tag.

   • $ docker pull mayushibata/rcc:latest
   • $ docker images # Verify that the image has been pulled successfully.

2. Run the container. Note: The port number for the container is fixed at 5000.

   • $ docker run -p (MACHINE_PORT):5000 -d (IMAGE_ID) # (MACHINE_PORT): Port number for handling app requests on the container's host machine. e.g. 30000
   • $ docker ps # Verify that the container status is "Up".

3. Use the app

   • Enter http://(MACHINE_IP):(MACHINE_PORT) (e.g.192.0.2.1:30000) in the web browser. # (MACHINE_PORT): The port number specified in step 2.
   • Use the app. Note: The job may take up to 180 seconds.

4. Pause and restart the container.

   • $ docker stop (CONTAINER_ID) # Pause. (CONTAINER_ID): Identify by executing $ docker ps -a
   • $ docker start (CONTAINER_ID) # Restart.