AIRMEC/im4MEC
Code for the im4MEC model described in the paper 'Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts'.
This tool helps pathologists and researchers classify endometrial cancer by analyzing whole-slide images (WSIs) stained with haematoxylin and eosin (H&E). It takes these H&E stained WSI images as input and predicts the molecular classification of the cancer. The output identifies key morphological features linked to the molecular type, providing an interpretable result for medical professionals.
No commits in the last 6 months.
Use this if you need to automatically and interpretably classify endometrial cancer from H&E stained pathology slides, linking visual patterns to molecular subtypes.
Not ideal if you are working with non-pathology images or need to classify other types of cancer, as it is specifically designed for endometrial cancer H&E WSIs.
Stars
54
Forks
16
Language
Python
License
GPL-3.0
Category
Last pushed
Feb 02, 2024
Commits (30d)
0
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