Echocardiogram video plays a crucial role in analysing cardiac function and diagnosing cardiac diseases. Current deep neural network methods primarily aim to enhance diagnosis accuracy by incorporating prior knowledge, such as segmenting cardiac structures or lesions annotated by human experts. However, diagnosing the inconsistent behaviours of the heart, which exist across both spatial and temporal dimensions, remains extremely challenging. For instance, the analysis of cardiac motion acquires both spatial and temporal information from the heartbeat cycle. To address this issue, we propose a novel reconstruction-based approach named CardiacNet to learn a better representation of local cardiac structures and motion abnormalities through echocardiogram videos. CardiacNet is accompanied by the Consistency Deformation Codebook (CDC) and the Consistency Deformed-Discriminator (CDD) to learn the commonalities across abnormal and normal samples by incorporating cardiac prior knowledge. In addition, we propose benchmark datasets named CardiacNet-PAH and CardiacNet-ASD to evaluate the effectiveness of cardiac disease assessment. In experiments, our CardiacNet can achieve state-of-the-art results in three different cardiac disease assessment tasks on public datasets CAMUS, EchoNet, and our datasets. The code and dataset are available at: https://github.com/xmed-lab/CardiacNet.

翻译：超声心动图视频在分析心脏功能和诊断心脏疾病中起着至关重要的作用。当前的深度神经网络方法主要通过融入先验知识（例如分割由人类专家标注的心脏结构或病变）来提高诊断准确性。然而，诊断心脏在空间和时间维度上均存在的不一致行为仍然极具挑战性。例如，分析心脏运动需要从心跳周期中获取空间和时间信息。为解决此问题，我们提出了一种名为CardiacNet的新型基于重构的方法，旨在通过超声心动图视频学习更好的局部心脏结构和运动异常表征。CardiacNet配备了**一致性形变码本**和**一致性形变判别器**，通过融入心脏先验知识来学习异常与正常样本间的共性。此外，我们提出了名为CardiacNet-PAH和CardiacNet-ASD的基准数据集，以评估心脏疾病评估的有效性。在实验中，我们的CardiacNet在公开数据集CAMUS、EchoNet以及我们自建数据集上的三种不同心脏疾病评估任务中均能取得最先进的结果。代码和数据集可在以下网址获取：https://github.com/xmed-lab/CardiacNet。