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.