[2312.00357] A Generalizable Deep Learning System for Cardiac MRI

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2312.00357 (eess) [Submitted on 1 Dec 2023 (v1), last revised 25 Mar 2026 (this version, v2)] Title:A Generalizable Deep Learning System for Cardiac MRI Authors:Rohan Shad, Cyril Zakka, Dhamanpreet Kaur, Mrudang Mathur, Robyn Fong, Joseph Cho, Ross Warren Filice, John Mongan, Kimberly Kalianos, Nishith Khandwala, David Eng, Matthew Leipzig, Walter R. Witschey, Alejandro de Feria, Victor A. Ferrari, Euan A. Ashley, Michael A. Acker, Curtis Langlotz, William Hiesinger View a PDF of the paper titled A Generalizable Deep Learning System for Cardiac MRI, by Rohan Shad and 18 other authors View PDF Abstract:Cardiac MRI allows for a comprehensive assessment of myocardial structure, function and tissue characteristics. Here we describe a foundational vision system for cardiac MRI, capable of representing the breadth of human cardiovascular disease and health. Our deep-learning model is trained via self-supervised contrastive learning, in which visual concepts in cine-sequence cardiac MRI scans are learned from the raw text of the accompanying radiology reports. We train and evaluate our model on data from four large academic clinical institutions in the United States. We additionally showcase the performance of our models on the UK BioBank and two additional publicly available external datasets. We explore emergent capabilities of our system and demonstrate remarkable performance across a range of tasks,...