Optimized multiple instance learning for brain tumor classification using weakly supervised contrastive learning

Brain tumors have a great impact on patients’ quality of life and accurate histopathological classification of brain tumors is crucial for patients’ prognosis. Multi-instance learning (MIL) has become the mainstream method for analyzing whole-slide images (WSIs). However, current MIL-based methods f...

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Veröffentlicht in:	Computers in biology and medicine Jg. 191; S. 110075
Hauptverfasser:	Lu, Kaoyan, Lin, Shiyu, Xue, Kaiwen, Huang, Duoxi, Ji, Yanghong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.06.2025
Schlagworte:	Algorithms Brain Neoplasms - classification Brain Neoplasms - diagnostic imaging Brain Neoplasms - pathology Brain tumor Classification Contrastive learning Cross-detection Humans Image Interpretation, Computer-Assisted - methods Internal Medicine Multiple instance learning Multiple-Instance Learning Algorithms Other Supervised Machine Learning Brain tumor Cross-detection Multiple instance learning Contrastive learning Classification
ISSN:	0010-4825, 1879-0534, 1879-0534
Online-Zugang:	Volltext
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