From e6ca72e18afa6f37a763dbf14ba194df1ecb750d Mon Sep 17 00:00:00 2001 From: muellerdo Date: Wed, 24 Jan 2024 15:56:52 +0100 Subject: [PATCH] docs: added docker usage example --- README.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/README.md b/README.md index a2ba160..5478856 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,9 @@ # DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks +[![shield_python](https://img.shields.io/pypi/pyversions/DeepGleason?style=flat-square)](https://www.python.org/) +[![shield_build](https://img.shields.io/github/actions/workflow/status/frankkramer-lab/DeepGleason/build-package.yml?branch=master&style=flat-square)](https://github.com/frankkramer-lab/DeepGleason) +[![shield_license](https://img.shields.io/github/license/frankkramer-lab/DeepGleason?style=flat-square)](https://www.gnu.org/licenses/gpl-3.0.en.html) + Welcome to DeepGleason, an advanced software solution developed for inferring Gleason grading in prostate whole-slide images using a deep neural network. Our API is designed to assist pathologists and researchers in accurately analyzing and grading prostate tissue samples. With a focus on precision and efficiency, DeepGleason integrates seamlessly into existing workflows, providing a reliable tool for enhancing the diagnostic process. Explore the capabilities of our software and streamline your prostate cancer assessment with DeepGleason's neutral and effective approach to Gleason grading. ![viz](docs/viz.png) @@ -47,6 +51,26 @@ optional arguments: output CSV containing predicted soft labels ``` +**Docker Usage:** + +```sh +# Pull the image from the Container Registry +docker pull ghcr.io/frankkramer-lab/deepgleason + +# Create a data directory and move whole-image slides into it +mkdir /home/main/DeepGleason.data +mv my_slide_1.tiff /home/main/DeepGleason.data/ +mv my_slide_2.tiff /home/main/DeepGleason.data/ +mv my_slide_3.tiff /home/main/DeepGleason.data/ + +# Run the DeepGleason container +docker run \ + -v /home/main/DeepGleason.data:/data --rm \ + ghcr.io/frankkramer-lab/deepgleason + +## all results will be stored in the created DeepGleason.data directory +``` + ## Installation Download Git repository: