Deep unsupervised clustering for prostate auto-segmentation with and without hydrogel spacer

Introduction. Clinical datasets for training deep learning (DL) models often exhibit high levels of heterogeneity due to differences such as patient characteristics, new medical techniques, and physician preferences. In recent years, hydrogel spacers have been used in some prostate cancer patients r...

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Hauptverfasser:	Hengrui Zhao, Biling Wang, Michael Dohopolski, Ti Bai, Steve Jiang, Dan Nguyen
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IOP Publishing 2025-01-01
Schriftenreihe:	Machine Learning: Science and Technology
Schlagworte:	medical image segmentation deep learning unsupervised learning clustering hydrogel spacer
Online-Zugang:	https://doi.org/10.1088/2632-2153/ada8f3
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