FR-MIL: Distribution Re-Calibration-Based Multiple Instance Learning With Transformer for Whole Slide Image Classification

In digital pathology, whole slide images (WSI) are crucial for cancer prognostication and treatment planning. WSI classification is generally addressed using multiple instance learning (MIL), alleviating the challenge of processing billions of pixels and curating rich annotations. Though recent MIL...

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Vydané v:	IEEE transactions on medical imaging Ročník 44; číslo 1; s. 409 - 421
Hlavní autori:	Chikontwe, Philip, Kim, Meejeong, Jeong, Jaehoon, Jung Sung, Hyun, Go, Heounjeong, Jeong Nam, Soo, Park, Sang Hyun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.01.2025
Predmet:	Algorithms Benchmark testing Feature extraction Histopathology Humans Image Interpretation, Computer-Assisted - methods Machine Learning Medical diagnostic imaging multiple instance learning Multiple-Instance Learning Algorithms Neoplasms - diagnostic imaging Neoplasms - pathology Task analysis Transformers Uncertainty Vectors weakly supervised learning whole slide images
ISSN:	0278-0062, 1558-254X, 1558-254X
On-line prístup:	Získať plný text
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