365-04-003 ISMRM Abstract

Interpretable machine learning model for differentiating US from ALM based on conventional MRI features and radiomics

Primary: Body - Urogenital

Secondary: Analysis Methods - Radiomics

365-04-003 · Body MR: Potpourri of Techniques in Uterine, Cervical, Liver, and Kidney Imaging · Monday, 11 May, 2:45 PM–3:40 PM · Digital Posters Row F

Keywords: Radiomics Magnetic Resonance Imaging (MRI) Machine learning Uterine sarcoma Uterine leiomyoma

Accepted

Zhong Yang¹, Chao Wei ², Mengxiao Liu³

¹Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China

²The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China

³MR Collaborations, Siemens Healthineers Ltd., Shanghai, China

Presenting Author: Chao Wei

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Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/365-04-003