Code-free machine learning for classification of central nervous system histopathology images.
Details
Serval ID
serval:BIB_E72186C9E68A
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Code-free machine learning for classification of central nervous system histopathology images.
Journal
Journal of neuropathology and experimental neurology
ISSN
1554-6578 (Electronic)
ISSN-L
0022-3069
Publication state
Published
Issued date
21/02/2023
Peer-reviewed
Oui
Volume
82
Number
3
Pages
221-230
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Publication Status: ppublish
Abstract
Machine learning (ML), an application of artificial intelligence, is currently transforming the analysis of biomedical data and specifically of biomedical images including histopathology. The promises of this technology contrast, however, with its currently limited application in routine clinical practice. This discrepancy is in part due to the extent of informatics expertise typically required for implementation of ML. Therefore, we assessed the suitability of 2 publicly accessible code-free ML platforms (Microsoft Custom Vision and Google AutoML), for classification of histopathological images of diagnostic central nervous system tissue samples. When trained with typically 100 to more than 1000 images, both systems were able to perform nontrivial classifications (glioma vs brain metastasis; astrocytoma vs astrocytosis, prediction of 1p/19q co-deletion in IDH-mutant tumors) based on hematoxylin and eosin-stained images with high accuracy (from ∼80% to nearly 100%). External validation of the predicted accuracy and negative control experiments were found to be crucial for verification of the accuracy predicted by the algorithms. Furthermore, we propose a possible diagnostic workflow for pathologists to implement classification of histopathological images based on code-free machine platforms.
Keywords
Humans, Artificial Intelligence, Mutation, Isocitrate Dehydrogenase/genetics, Brain Neoplasms/pathology, Machine Learning, Central Nervous System/pathology, Astrocytoma, Digital pathology, Glioma, Machine learning, Oligodendroglioma
Pubmed
Web of science
Open Access
Yes
Create date
10/03/2023 12:16
Last modification date
18/03/2023 6:45