Alleviating Class Imbalance in Semi-Supervised Multi-Organ Segmentation via Balanced Subclass Regularization

Semi-supervised learning (SSL) has shown notable potential in relieving the heavy demand of dense prediction tasks on large-scale well-annotated datasets, especially for the challenging multi-organ segmentation (MoS). However, the prevailing class-imbalance problem in MoS, caused by the substantial...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE signal processing letters 2024, Vol.31, p.2450-2454
Hauptverfasser:	Feng, Zhenghao, Wen, Lu, Yan, Binyu, Cui, Jiaqi, Wang, Yan
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy balanced subclass regularization Data models Decoding Image segmentation Knowledge engineering multi-organ segmentation Multitasking semi-supervised learning Task analysis
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!