PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation

• Published in: Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Vol. 2024; pp. 11736 - 11746
• Main Authors: 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
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.06.2024
• Subjects: Computational modeling; digital pathology; dynamic network; Head; hierarchical taxonomy; Image segmentation; Pathology; segmentation; semi-supervised learning; Semisupervised learning; Training; Transforms

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...