Fig. 1
Development of a multi-class classification model of CNS tissue using CNNs. a H&E-stained WSI of a glioblastoma containing a heterogeneous mixture of tumor, necrosis, brain tissue, blood and surgical material. Black scale bar represents 4 mm. b Examples of image tiles for the 13 classes used for CNN training are shown. Images have been magnified to ~ 250 μm2 to highlight key diagnostic features. c-e WSI-level annotations are carried through automated tiling and classification of 1024 × 1024 pixel image patches using our trained CNN. Class activation maps (CAMs) are generated by reassembly of classified tiles to provide a global overview of lesion localization (brown). Black scale bar represents 2 mm. f Immunohistochemistry for IDH1-R132H shows the associated “ground truth” for this glioma. g H&E section of a metastatic carcinoma (left panel), associated ground truth (middle panel, p40 immunostaining) and the lesional coordinates (brown) predicted by the CNN. The aggregate probability scores generated by the final softmax function allows for global estimates of the various tissue types found on each WSI. Black scale bar represents 3 mm