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.

该论文介绍了一种自适应锁定不可知网络（ALAN）的方法，通过自我监督的视觉特征提取和解剖鲁棒的语义自分割来应对医学成像应用中标记数据不足的问题，并应用于三个公开的超声心动图数据集，结果表明这种方法能够稳定地识别心脏的解剖子区，并设计了两个下游模型用于解剖区域分割和超声心动图视图分类。