Deep unsupervised clustering for prostate auto-segmentation with and without hydrogel spacer
Automatic clustering assisted by deep learning can reveal hidden data clusters in clinical datasets, and CLIP-UNet effectively utilizes clustered labels and achieves higher performance.
Authors
- Michael Dohopolski (ORCID: https://orcid.org/0000-0002-9043-1490)
- Hengrui Zhao (ORCID: https://orcid.org/0000-0002-6712-5823)
- Ti Bai (ORCID: https://orcid.org/0000-0002-6697-7434)
- Biling Wang (ORCID: https://orcid.org/0000-0002-6894-8645)
- Dan Nguyen (ORCID: https://orcid.org/0000-0002-9590-0655)
- Steve Jiang
Institutions
- The University of Texas Southwestern Medical Center (US)
Publication Details
- Journal
- Machine Learning Science and Technology
- Published
- 2025-01-10
- DOI
- https://doi.org/10.1088/2632-2153/ada8f3
- Citations
- 2
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 3.04