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Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: an experimental study
- We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet representations for a target domain of histopathological images, and study the impact of in-domain adaptation using self-supervised and multi-task learning approaches for pretraining the models using the medium-to-large scale datasets of histopathological images. A semi-supervised learning approach is furthermore proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images (WSI). The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning with balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the pathologist's efforts for annotation. Finally, we provide a visualization tool working at WSI level which generates heatmaps that highlight tumor areas; thus, providing insights to pathologists concerning the most informative parts of the WSI.
Author: | Vladimir DespotovicORCiD, Sang-Yoon Kim, Ann-Christin HauORCiD, Aliaksandra KakoichankavaORCiD, Gilbert Georg KlammingerORCiDGND, Felix Bruno Kleine BorgmannORCiD, Katrin Barbara Magda FrauenknechtORCiDGND, Michel Guy André MittelbronnORCiDGND, Petr V. NazarovORCiD |
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URN: | urn:nbn:de:hebis:30:3-834349 |
DOI: | https://doi.org/10.1016/j.heliyon.2024.e27515 |
ISSN: | 2405-8440 |
Parent Title (English): | Heliyon |
Publisher: | Elsevier |
Place of publication: | Amsterdam |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2024/03/06 |
Date of first Publication: | 2024/03/06 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2024/04/15 |
Tag: | Deep learning; Digital pathology; Glioma; Transfer learning; Whole slide images |
Volume: | 10 |
Issue: | 5, e27515 |
Article Number: | e27515 |
Page Number: | 14 |
HeBIS-PPN: | 522320775 |
Institutes: | Medizin |
Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Sammlungen: | Universitätspublikationen |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |