PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation

Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics, treatment evaluation, and clinical research. The complex kidney system comprises various components across multiple levels, including regions (cortex, medulla), functional units (glomeruli, tubules), and cells...

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Vydané v:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Ročník 2024; s. 11736 - 11746
Hlavní autori:	Deng, Ruining, Liu, Quan, Cui, Can, Yao, Tianyuan, Yue, Jialin, Xiong, Juming, Yu, Lining, Wu, Yifei, Yin, Mengmeng, Wang, Yu, Zhao, Shilin, Tang, Yucheng, Yang, Haichun, Huo, Yuankai
Médium:	Konferenčný príspevok.. Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.06.2024
Predmet:	Computational modeling digital pathology dynamic network Head hierarchical taxonomy Image segmentation Pathology segmentation semi-supervised learning Semisupervised learning Training Transforms
ISSN:	1063-6919, 1063-6919
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