Interaction of a priori Anatomic Knowledge with Self-Supervised Contrastive Learning in Cardiac Magnetic Resonance Imaging

Makiya Nakashima; Inyeop Jang; Ramesh Basnet; Mitchel Benovoy; W. H. Wilson Tang; Christopher Nguyen; Deborah Kwon; Tae Hyun Hwang; David Chen

arXiv · 2205.12429

Interaction of a priori Anatomic Knowledge with Self-Supervised Contrastive Learning in Cardiac Magnetic Resonance Imaging

Makiya Nakashima·Inyeop Jang·Ramesh Basnet·Mitchel Benovoy·W. H. Wilson Tang·Christopher Nguyen·Deborah Kwon·Tae Hyun Hwang·David Chen

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Abstract

Training deep learning models on cardiac magnetic resonance imaging (CMR) can be a challenge due to the small amount of expert generated labels and inherent complexity of data source. Self-supervised contrastive learning (SSCL) has recently been shown to boost performance in several medical imaging tasks. However, it is unclear how much the pre-trained representation reflects the primary organ of interest compared to spurious surrounding tissue. In this work, we evaluate the optimal method of incorporating prior knowledge of anatomy into a SSCL training paradigm. Specifically, we evaluate using a segmentation network to explicitly local the heart in CMR images, followed by SSCL pretraining in multiple diagnostic tasks. We find that using a priori knowledge of anatomy can greatly improve the downstream diagnostic performance. Furthermore, SSCL pre-training with in-domain data generally improved downstream performance and more human-like saliency compared to end-to-end training and ImageNet pre-trained networks. However, introducing anatomic knowledge to pre-training generally does not have significant impact.

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Makiya Nakashima, Inyeop Jang, Ramesh Basnet, Mitchel Benovoy, W. H. Wilson Tang, Christopher Nguyen, Deborah Kwon, Tae Hyun Hwang, David Chen. 2022-05-25. Interaction of a priori Anatomic Knowledge with Self-Supervised Contrastive Learning in Cardiac Magnetic Resonance Imaging. https://arxiv.org/abs/2205.12429

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