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.

翻译：深度学习模型在医学影像应用中的监督训练需要大量标注数据。由于这些图像需要由医疗专业人员标注，这带来了挑战。为解决这一局限，我们引入了自适应锁定无源网络（ALAN），该概念利用大型骨干模型进行自监督视觉特征提取，以生成解剖学上鲁棒的语义自分割。在ALAN方法中，这种自监督训练仅在大规模多样化数据集上执行一次。由于分割的直观可解释性，可以使用少量参数的白箱模型轻松设计针对特定任务的下游模型。这反过来为与领域专家沟通模型内部运作机制并引入先验知识开辟了可能性。此外，与完全监督方法相比，下游模型对数据的需求也更少。这些特性使ALAN特别适用于资源稀缺场景，例如昂贵的临床试验和罕见疾病。本文在三个公开可用的超声心动图数据集上应用了ALAN方法：EchoNet-Dynamic、CAMUS和TMED-2。我们的研究表明，自监督骨干模型能够鲁棒地识别心尖四腔切面中心脏的解剖亚区域。在此基础上，我们设计了两个下游模型：一个用于分割目标解剖区域，另一个用于超声心动图视图分类。