@inbook{8bb787f3598645d8b317b2981778398b,
title = "Contrastive Learning for View Classification of Echocardiograms",
abstract = "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 na{\"i}ve 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.",
keywords = "Classification, Contrastive learning, Echocardiography",
author = "Agisilaos Chartsias and Shan Gao and Angela Mumith and Jorge Oliveira and Kanwal Bhatia and Bernhard Kainz and Arian Beqiri",
note = "Funding Information: We thank the echocardiographers involved in this study for their thorough annotation of images from the EVAREST dataset. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 2nd International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 27-09-2021",
year = "2021",
doi = "10.1007/978-3-030-87583-1_15",
language = "English",
isbn = "9783030875824",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "149--158",
editor = "Noble, {J. Alison} and Stephen Aylward and Alexander Grimwood and Zhe Min and Su-Lin Lee and Yipeng Hu",
booktitle = "Simplifying Medical Ultrasound - Second International Workshop, ASMUS 2021, Held in Conjunction with MICCAI 2021, Proceedings",
address = "Germany",
}