AttriMIL: Revisiting attention-based multiple instance learning for whole-slide pathological image classification from a perspective of instance attributes

Multiple instance learning (MIL) is a powerful approach for whole-slide pathological image (WSI) analysis, particularly suited for processing gigapixel-resolution images with slide-level labels. Recent attention-based MIL architectures have significantly advanced weakly supervised WSI classification...

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Vydané v:	Medical image analysis Ročník 103; s. 103631
Hlavní autori:	Cai, Linghan, Huang, Shenjin, Zhang, Ye, Lu, Jinpeng, Zhang, Yongbing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier B.V 01.07.2025
Predmet:	Algorithms Attribute scoring mechanism Humans Image Interpretation, Computer-Assisted - methods Machine Learning Multiple instance learning Multiple-Instance Learning Algorithms Pathological image analysis Pathology adaptive learning Pathology attribute constraint Pathology attribute constraint Pathological image analysis Attribute scoring mechanism Multiple instance learning Pathology adaptive learning
ISSN:	1361-8415, 1361-8423, 1361-8423
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