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

Source: [Interaction of a priori Anatomic Knowledge with Self-Supervised Contrastive Learning in Cardiac Magnetic Resonance Imaging](<https://www.sota2.com/research/paper/68fa6ca41e209131cea35266>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:15:46.115Z

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

Published: 2022-05-25T01:33:37.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2205.12429>)

[PDF](<https://arxiv.org/pdf/2205.12429.pdf>)