Unlocking the Heart Using Adaptive Locked Agnostic Networks

TL;DR

This paper introduces ALAN, a self-supervised learning approach for medical imaging that produces interpretable, robust features, reducing data needs and enhancing model transparency, especially useful in resource-limited clinical scenarios.

Contribution

The paper presents ALAN, a novel self-supervised framework that generates anatomically meaningful features for medical imaging, enabling easier downstream task modeling with fewer labeled data.

Findings

ALAN's backbone accurately identifies heart subregions in echocardiograms.

Downstream models built on ALAN features perform well in segmentation and view classification.

ALAN reduces data requirements for effective medical image analysis.

Abstract

Supervised training of deep learning models for medical imaging applications requires a significant amount of labeled data. This is posing a challenge as the images are required to be annotated by medical professionals. To address this limitation, we introduce the Adaptive Locked Agnostic Network (ALAN), a concept involving self-supervised visual feature extraction using a large backbone model to produce anatomically robust semantic self-segmentation. In the ALAN methodology, this self-supervised training occurs only once on a large and diverse dataset. Due to the intuitive interpretability of the segmentation, downstream models tailored for specific tasks can be easily designed using white-box models with few parameters. This, in turn, opens up the possibility of communicating the inner workings of a model with domain experts and introducing prior knowledge into it. It also means that the downstream models become less data-hungry compared to fully supervised approaches. These characteristics make ALAN particularly well-suited for resource-scarce scenarios, such as costly clinical trials and rare diseases. In this paper, we apply the ALAN approach to three publicly available echocardiography datasets: EchoNet-Dynamic, CAMUS, and TMED-2. Our findings demonstrate that the self-supervised backbone model robustly identifies anatomical subregions of the heart in an apical four-chamber view. Building upon this, we design two downstream models, one for segmenting a target anatomical region, and a second for echocardiogram view classification.