Interaction of a priori Anatomic Knowledge with Self-Supervised Contrastive Learning in Cardiac Magnetic Resonance Imaging
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 26 references that are in the map. First posted on arXiv 25 May 2022.
Builds on
13 of 26 references.
References · 13
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
- 2019Transfusion: Understanding Transfer Learning for Medical Imagingcited by 366
- 2020Contrastive learning of global and local features for medical image segmentation with limited annotationscited by 309
- 2019Noise2Self: Blind Denoising by Self-Supervisioncited by 274
- 2017Representation Learning by Learning to Countcited by 120
- 2020Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotationscited by 89
- 2019Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Predictioncited by 70
- 2019Multimodal Self-Supervised Learning for Medical Image Analysiscited by 57
- 2021CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretationcited by 57
- 2018Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifierscited by 35
- 2019Embedding Human Knowledge into Deep Neural Network via Attention Mapcited by 33
1 more
Built on by
Citing papers per year: 2023: 1.
1 paper.