Disentangled Pseudo-Bag Augmentation for Whole Slide Image Multiple Instance Learning

As the predominant approach for pathological whole slide image (WSI) classification, multiple instance learning (MIL) methods struggle with limited labeled WSIs. Although MIL has achieved notable progress with pseudo-bag-oriented augmentation methods, their effectiveness is often constrained by nois...

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Vydané v:	IEEE transactions on medical imaging Ročník 44; číslo 11; s. 4181 - 4197
Hlavní autori:	Dong, Jiuyang, Jiang, Junjun, Jiang, Kui, Li, Jiahan, Cai, Linghan, Zhang, Yongbing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.11.2025
Predmet:	Accuracy Algorithms Cancer Data augmentation Data mining Feature extraction Humans Image Interpretation, Computer-Assisted - methods Machine Learning multiple instance learning Multiple-Instance Learning Algorithms Neural Networks, Computer Pathology Phenotypes Probability Training Tumors Whole slide image classification
ISSN:	0278-0062, 1558-254X, 1558-254X
On-line prístup:	Získať plný text
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