![]() ![]() For the second use case, the best performing CNN achieved an area under the curve (AUC) of 0.97 for the receiver operating characteristic (ROC) for corticotroph adenoma, 0.86 for silent corticotroph adenoma, and 0.98 for gonadotroph adenoma. Results: Our DNET-GG classifier achieved an AUC of 1.00 for the ROC curve. In the same approach, we also predicted clinically silent corticotroph adenoma. Here we trained a CNN to predict the hormone expression profile of pituitary adenomas. Within the second use case, we included four clinicopathological disease conditions in a multilabel approach. We trained a convolutional neuronal network (CNN) to distinguish between dysembryoplastic neuroepithelial tumor (DNET) and ganglioglioma (GG), two neuropathological low-grade epilepsy-associated tumor entities. The first use case was a two-class classification problem. ![]() ![]() Methods: Two histopathology use-cases were selected and only hematoxylin and eosin (H&:E) stained slides were used. We developed an open-source library dealing with recurrent tasks in the processing of WSI and helping with the training and evaluation of neuronal networks for classification tasks. Background: Processing whole-slide images (WSI) to train neural networks can be intricate and labor intensive.
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