# Contrastive Learning for View Classification of Echocardiograms

arXiv: https://arxiv.org/abs/2108.03124 · HTML: https://mlsanity.com/paper/2108.03124/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 6 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017)
- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound](https://mlsanity.com/paper/1612.05601/) (2016)
- [Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound](https://mlsanity.com/paper/2008.06607/) (2020)
- [Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model](https://mlsanity.com/paper/1806.07497/) (2018)

## Built on by (most cited first, 7 of 7)

- [Contrastive Pretraining for Echocardiography Segmentation with Limited Data](https://mlsanity.com/paper/2201.07219/) (2022)
- [Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis](https://mlsanity.com/paper/2207.11581/) (2022)
- [CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound](https://mlsanity.com/paper/2206.08524/) (2022)
- [Automatic retrieval of corresponding US views in longitudinal examinations](https://mlsanity.com/paper/2306.04739/) (2023)
- [Unlocking the Heart Using Adaptive Locked Agnostic Networks](https://mlsanity.com/paper/2309.11899/) (2023)
- [Fully Automatic Data Labeling for Ultrasound Screen Detection](https://mlsanity.com/paper/2511.13197/) (2025)
- [Benchmarking Self-Supervised Models for Cardiac Ultrasound View Classification](https://mlsanity.com/paper/2602.15339/) (2026)

## Abstract (arXiv)

Analysis of cardiac ultrasound images is commonly performed in routine clinical practice for quantification of cardiac function. Its increasing automation frequently employs deep learning networks that are trained to predict disease or detect image features. However, such models are extremely data-hungry and training requires labelling of many thousands of images by experienced clinicians. Here we propose the use of contrastive learning to mitigate the labelling bottleneck. We train view classification models for imbalanced cardiac ultrasound datasets and show improved performance for views/classes for which minimal labelled data is available. Compared to a naive baseline model, we achieve an improvement in F1 score of up to 26% in those views while maintaining state-of-the-art performance for the views with sufficiently many labelled training observations.