Trained deep neural networks for MSI/dMMR detection in colorectal cancer histology

These are trained neural network models in PyTorch format to process tessellated images of colorectal cancer histology samples. The input is expected to be 224x224 px RGB image tiles normalized with the Macenko method. The output is a probability of the image tile for being MSI/dMMR or MSS/pMMR. 

The models have been trained on eight cohorts but not on the validation cohort. The validation cohorts are:

Ex_0  : DACHS

Ex_1  :  DUSSEL

Ex_2  :  MECC

Ex_3  : QUASAR

Ex_4  : RAINBOW

Ex_5  : TCGA

Ex_6  : UMM

Ex_7  : YORKSHIRE

Ex_8  : MUNICH

The models can be loaded in Python with 

>>> model = torch.load(path, map_location=torch.device('cpu'))

Further details are given in the manuscript.