Jungo, Patric; Hewer, Ekkehard Walter (2023). Code-free machine learning for classification of central nervous system histopathology images. Journal of neuropathology and experimental neurology, 82(3), pp. 221-230. Oxford University Press 10.1093/jnen/nlac131
Full text not available from this repository.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.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
04 Faculty of Medicine > Service Sector > Institute of Pathology |
UniBE Contributor: |
Jungo, Patric, Hewer, Ekkehard Walter |
Subjects: |
500 Science > 570 Life sciences; biology 600 Technology > 610 Medicine & health |
ISSN: |
1554-6578 |
Publisher: |
Oxford University Press |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
06 Feb 2023 09:53 |
Last Modified: |
23 Feb 2023 00:16 |
Publisher DOI: |
10.1093/jnen/nlac131 |
PubMed ID: |
36734664 |
Uncontrolled Keywords: |
Astrocytoma Digital pathology Glioma Machine learning Oligodendroglioma |
URI: |
https://boris.unibe.ch/id/eprint/178348 |
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