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

Neuner C, Coras R, Blümcke I, Popp A, Schlaffer SM, Wirries A, Buchfelder M, Jabari S (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Applied Sciences MDPI

Book Volume: 12

Journal Issue: 1

DOI: 10.3390/app12010013

Abstract

Background: Processing whole-slide images (WSI) to train neural networks can be intricate and labor intensive. 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. Methods: Two histopathology use-cases were selected and only hematoxylin and eosin (H&E) stained slides were used. The first use case was a two-class classification problem. 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. Within the second use case, we included four clinicopathological disease conditions in a multilabel approach. Here we trained a CNN to predict the hormone expression profile of pituitary adenomas. In the same approach, we also predicted clinically silent corticotroph adenoma. Results: Our DNET-GG classifier achieved an AUC of 1.00 for the ROC curve. 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. All scores were calculated with the help of our library on predictions on a case basis. Conclusions: Our comprehensive and fastai-compatible library is helpful to standardize the workflow and minimize the burden of training a CNN. Indeed, our trained CNNs extracted neuropathologically relevant information from the WSI. This approach will supplement the clinicopathological diagnosis of brain tumors, which is currently based on cost-intensive microscopic examination and variable panels of immunohistochemical stainings.

Authors with CRIS profile

Roland Coras Institute of Neuropathology Ingmar Blümcke Lehrstuhl für Neuropathologie Sven-Martin Schlaffer Lehrstuhl für Neurochirurgie Michael Buchfelder Lehrstuhl für Neurochirurgie Samir Jabari Institute of Neuropathology

Additional Organisation(s)

Interdisziplinäres Zentrum für Klinische Forschung

Related research project(s)

J081: Identification of imaging biomarkers in Low Grade Epilepsy Associated Tumors with deep learning algorithms Jan. 1, 2020 - Dec. 31, 2022

Involved external institutions

Hessing Stiftung DE Germany (DE)

How to cite

APA:

Neuner, C., Coras, R., Blümcke, I., Popp, A., Schlaffer, S.-M., Wirries, A.,... Jabari, S. (2022). A Whole-Slide Image Managing Library Based on Fastai for Deep Learning in the Context of Histopathology: Two Use-Cases Explained. Applied Sciences, 12(1). https://dx.doi.org/10.3390/app12010013

MLA:

Neuner, Christoph, et al. "A Whole-Slide Image Managing Library Based on Fastai for Deep Learning in the Context of Histopathology: Two Use-Cases Explained." Applied Sciences 12.1 (2022).

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