Unlocking the Heart Using Adaptive Locked Agnostic Networks
Sylwia Majchrowska Anders Hildeman Philip Teare Tom Diethe
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.