Unlocking Hearts by Locking Models: Solving Multiple Echocardiogram Problems Using a Holistic Self-Supervised Approach

Supervised training of deep learning models for medical imaging applications requires a significant amount of labeled data, which can be challenging to acquire as it necessitates the expertise of highly skilled medical professionals to annotate a large number of image sequences. To overcome this limitation, we propose the Adaptive Locked Agnostic Network (ALAN) concept for label-free, self-supervised learning of visual features. The advantage of this methodology is that the self-supervised training can be performed once on a large unlabeled dataset, and the features discovered can then be used on a plethora of subsequent downstream tasks. Since these self-engineered features have rich informational content, downstream tasks can be solved with more straightforward and less data-hungry methods than in fully-supervised approaches. In this paper, we perform two typical downstream tasks for echocardiogram data: segmentation of anatomical regions and classification of the echocardiogram views. We evaluate the proposed self-supervised approach on three publicly available echocardiogram datasets: EchoNet-Dynamic, CAMUS, and TMED-2.