Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Whole Slide Image Managing Library Based on Fastai for Deep Learning in the Context of Histopathology: Two Use-Cases Explained

Version 1 : Received: 22 October 2021 / Approved: 25 October 2021 / Online: 25 October 2021 (14:51:22 CEST)
Version 2 : Received: 25 October 2021 / Approved: 26 October 2021 / Online: 26 October 2021 (14:10:11 CEST)

How to cite: Neuner, C.; Coras, R.; Blümcke, I.; Popp, A.; Schlaffer, S.M.; Buchfelder, M.; Jabari, S. A Whole Slide Image Managing Library Based on Fastai for Deep Learning in the Context of Histopathology: Two Use-Cases Explained. Preprints 2021, 2021100359 (doi: 10.20944/preprints202110.0359.v1). Neuner, C.; Coras, R.; Blümcke, I.; Popp, A.; Schlaffer, S.M.; Buchfelder, M.; Jabari, S. A Whole Slide Image Managing Library Based on Fastai for Deep Learning in the Context of Histopathology: Two Use-Cases Explained. Preprints 2021, 2021100359 (doi: 10.20944/preprints202110.0359.v1).

Abstract

Background: Processing whole-slide images (WSI) to train neural networks can be intricate and laborious. We developed an open-source library covering recurrent tasks in processing of WSI and in evaluating the performance of the trained networks for classification tasks. Methods: Two histopathology use-cases were selected. First we aimed to train a CNN to distinguish H&E-stained slides obtained from neuropathologically classified low-grade epilepsy-associated dysembryoplastic neuroepithelial tumor (DNET) and ganglioglioma (GG). The second project we trained a convolutional neural network (CNN) to predict the hormone expression of pituitary adenoms only from hematoxylin and eosin (H&E) stained slides. In the same approach, we addressed the issue to also predict clinically silent corticotroph adenoma. We included four clinico-pathological disease conditions in a multilabel approach. Results: Our 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. Our DNET-GG classifier achieved an AUC of 1.00 for the ROC curve. All scores were calculated with the help of our library on predictions on a case basis. Conclusions: Our comprehensive library is most helpful to standardize the work-flow and minimize the work-burden in training CNN. It is also compatible with fastai. Indeed, our new CNNs reliably extracted neuropathologically relevant information from the H&E staining only. This approach will supplement the clinico-pathological diagnosis of brain tumors, which is currently based on cost-intense microscopic examination and variable panels of immunohistochemical stainings.

Keywords

brain; pituitary adenoma; Dysembryoplastic neuroepithelial tumor; DNET; Ganglioglioma; deep learning; digital pathology; convolutional neural network; computer vision; machine learning; convolutional neural network; CNN

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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