Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning.

ABSTRACT:

Background

Advances in self-supervised learning (SSL) have enabled state-of-the-art automated medical image diagnosis from small, labeled datasets. This label efficiency is often desirable, given the difficulty of obtaining expert labels for medical image recognition tasks. However, most efforts toward SSL in medical imaging are not adapted to video-based modalities, such as echocardiography.

Methods

We developed a self-supervised contrastive learning approach, EchoCLR, for echocardiogram videos with the goal of learning strong representations for efficient fine-tuning on downstream cardiac disease diagnosis. EchoCLR pretraining involves (i) contrastive learning, where the model is trained to identify distinct videos of the same patient, and (ii) frame reordering, where the

SUBMITTER: Holste G 

PROVIDER: S-EPMC11227494 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature