Self-supervised multi-modal alignment for whole body medical imaging

Conference item

This paper explores the use of self-supervised deep learning in medical imaging in cases where two scan modalities are available for the same subject. Specifically, we use a large publicly-available dataset of over 20,000 subjects from the UK Biobank with both whole body Dixon technique magnetic resonance (MR) scans and also dual-energy x-ray absorptiometry (DXA) scans. We make three contributions: (i) We introduce a multi-modal image-matching contrastive framework, that is able to learn to m...

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Authors: Windsor, R; Jamaludin, A; Kadir, T; Zisserman, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer Publisher's website
• Host title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
• Series: Lecture Notes in Computer Science
• Volume: 12902
• Pages: 90-101
• Publication date: 2021-09-21
• Acceptance date: 2021-06-11
• Event title: 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021)
• Event location: Virtual Event
• Event website: https://miccai2021.org/
• Event start date: 2021-09-27
• Event end date: 2021-10-01
• DOI: 10.1007/978-3-030-87196-3_9
• ISSN: 0302-9743
• EISBN: 978-3-030-87196-3
• ISBN: 978-3-030-87195-6
• Language: English
• Keywords: self-supervised learning; multi-modal imaging; deep learning; FFR