Work

Deep Learning–based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Biparametric MRI Datasets

Year: 2024

Type: article

Abstract: To determine whether the unsupervised domain adaptation (UDA) method with generated images improves the performance of a supervised learning (SL) model for prostate cancer (PCa) detection using multisite biparametric (bp) MRI datasets.

Source: Radiology Artificial Intelligence

DOI: 10.1148/ryai.230521

Authors: Hao Li, Han Liu, Heinrich von Busch, Robert Grimm, Henkjan Huisman, Angela Tong, David Winkel, Tobias Penzkofer, Ivan Shabunin, Moon Hyung Choi, Qingsong Yang, Dieter Szolar, Steven Shea, Fergus Coakley, Mukesh Harisinghani, Ipek Oguz, Dorin Comaniciu, Ali Kamen, Bin Lou

Institutions: Radboud University Nijmegen, Vanderbilt University, University Hospital of Basel, Radboud University Medical Center, Massachusetts General Hospital, Siemens Healthineers (Germany), New York University, Charité - Universitätsmedizin Berlin, Catholic University of Korea

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W4401793820

FWCI: 1.181

Cites: 23

Cited by: 8

Related to: 10