- WSI pre-processing tools are available at https://github.com/IARCbioinfo/WSIPreprocessing
- This repository contains:
- Scripts for dividing WSI into patches, called tiles.
- Scripts to normalize the HE/HES coloring of WSIs, used to artificially remove saffron coloring from WSIs produced in the French center.
Tumor segmentation with CFlow AD:
- Tumour areas were segmented unsupervised using the CFLow anomaly detection model. An adaptation of this model for this task is available at https://github.com/IARCbioinfo/TumorSegmentationCFlowAD
- This repository contains: + Scripts to train and evaluate the model + Script to create the segmentation maps
- Training sets for Ki-67 and HE/HES WSI are available on request from mathiane[at]iarc[dot]who[int], as is the pre-trained model (will be available on a server soon).
Automatic assessment of lung neuroendocrine neoplasms (LNEN) proliferative activity using Pathonet
- The Ki-67 and PHH3 indices were quantified automatically using the Pathonet supervised deep learning model. An adaptation of this model is available at the following address https://github.com/IARCbioinfo/PathonetLNEN
- This repository also contains scripts for calculating spatial metrics using graph theory as proposed by Bullloni and colleagues See : Automated analysis of proliferating cells spatial organization predicts prognosis in lung neuroendocrine neoplasms, Cancers 2021
- Annotated LNEN tiles and network weights are available on request from [email protected].
WSI features extraction using Barlow Twins
- The unsupervised deep learning model called Barlow Twins, proposed by J. Zbontar and colleagues, was used to extract the features of the tiles composing the HE-stained WSIs of LNEN patients. The adaptation of the method to the pathology that we have developed in PyTorch is available at https://github.com/IARCbioinfo/LNENBarlowTwins.
- LNEN pre-processed tiles and network weights are available on request from [email protected].
- 🚧 Some stats script