Abstract
Cardiac amyloidosis (CA) is a rare cardiomyopathy, with typical abnormalities in clinical measurements from echocardiograms such as reduced global longitudinal strain of the myocardium. An alternative approach for detecting CA is via neural networks, using video classification models such as convolutional neural networks. These models process entire video clips, but provide no assurance that classification is based on clinically relevant features known to be associated with CA. An alternative paradigm for disease classification is to apply models to quantitative features such as strain, ensuring that the classification relates to clinically relevant features. Drawing inspiration from this approach, we explicitly constrain a transformer model to the anatomical region where many known CA abnormalities occur -- the myocardium, which we embed as a set of deforming points and corresponding sampled image patches into input tokens. We show that our anatomical constraint can also be applied to the popular self-supervised learning masked autoencoder pre-training, where we propose to mask and reconstruct only anatomical patches. We show that by constraining both the transformer and pre-training task to the myocardium where CA imaging features are localized, we achieve increased performance on a CA classification task compared to full video transformers. Our model provides an explicit guarantee that the classification is focused on only anatomical regions of the echo, and enables us to visualize transformer attention scores over the deforming myocardium.
Abstract (translated)
心脏淀粉样变性（CA）是一种罕见的心肌病，其临床测量中的典型异常包括心肌整体纵向应变降低。检测 CA 的另一种方法是利用神经网络，如卷积神经网络进行视频分类模型的应用。这些模型处理整个视频片段，但无法保证分类基于与 CA 相关的临床重要特征。疾病分类的一种替代范例是将模型应用于诸如应变等定量特性上，从而确保分类与临床相关特征有关。 受到这一方法的启发，我们明确限制了一个转换器模型只关注许多已知 CA 异常发生的心肌解剖区域——我们将心肌嵌入到输入令牌中的一组变形点和相应的采样图像补丁。我们展示了这种解剖学约束也可以应用于流行的自我监督学习掩码自动编码器预训练，其中我们建议仅对解剖补丁进行掩码和重建。我们表明，通过将转换器及其预训练任务限制在 CA 影像特征局部化的区域（心肌），与完整视频转换器相比，在 CA 分类任务中实现了更高的性能。 我们的模型提供了明确的保证：分类仅关注回声图像中的解剖学区域，并使我们能够可视化变换器注意分数在整个变形的心肌上。
URL
https://arxiv.org/abs/2509.19691
