Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study.
Heliyon
; 10(5): e27515, 2024 Mar 15.
Article
in En
| MEDLINE
| ID: mdl-38562501
ABSTRACT
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Heliyon
Year:
2024
Document type:
Article
Affiliation country:
Luxembourg
Country of publication:
United kingdom