This repository contains the Inference script and the final trained model that was submitted to the [Automated Gleason Grading Challenge 2022] (https://aggc22.grand-challenge.org/). If you use parts of this Repository please cite the following work:
Challenge Paper: Huo, Xinmi, et al. "Comprehensive AI Model Development for Gleason Grading: From Scanning, Cloud-Based Annotation to Pathologist-AI Interaction" (Preprint available under: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4172090)
International Conference on Medical Image Computing and Computer-Assisted Intervention.
Cham: Springer Nature Switzerland, 2022.
And you must give credit to the submission paper:
Maximilian Fischer, Lukas Klein, Lars Krämer, Sebastian Ziegler: Semi-supervised automated Gleason Grading on WSI. AGGC Challenge 2022
Please download the pretrained model via the following link: https://zenodo.org/records/10986699
The submitted model achieved an overall accuracy throughout the test set of 0.7131.
- Create a new conda new conda environment with the recent version of Python,
as an example:
conda create --name AGGC python=3.9 - Install pytorch with the most recent CUDA version by following the instructions on the PyTorch Website.
- Clone this repository and install its dependencies:
https://github.com/MIC-DKFZ/AGGC2022.git
cd AGGC2022
pip install -e .
Copyright German Cancer Research Center (DKFZ) and contributors. Please make sure that your usage of this code is in compliance with its license.
This work was partially funded by Helmholtz Imaging (HI), a platform of the Helmholtz Incubator on Information and Data Science, by the Research Campus M2OLIE, which was funded by the German Federal Ministry of Education and Research (BMBF) within the Framework “Forschungscampus: Public-private partnership for Innovations” under the funding code 13GW0388A, by DKTK Joint Funding UPGRADE, Project “Subtyping of pancreatic cancer based on radiographic and pathological features“(SUBPAN), and by the Deutsche Forschungsgemeinschaft (DFG,German Research Foundation) under the grant 410981386.