Hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) are both heart
conditions that can progress to heart failure if untreated. They exhibit
similar echocardiographic characteristics, often leading to diagnostic
challenges. This paper introduces a novel multi-view deep learning approach
that utilizes 2D echocardiography for differentiating between HCM and CA. The
method begins by classifying 2D echocardiography data into five distinct
echocardiographic views: apical 4-chamber, parasternal long axis of left
ventricle, parasternal short axis at levels of the mitral valve, papillary
muscle, and apex. It then extracts features of each view separately and
combines five features for disease classification. A total of 212 patients
diagnosed with HCM, and 30 patients diagnosed with CA, along with 200
individuals with normal cardiac function(Normal), were enrolled in this study
from 2018 to 2022. This approach achieved a precision, recall of 0.905, and
micro-F1 score of 0.904, demonstrating its effectiveness in accurately
identifying HCM and CA using a multi-view analysis.

通过多视角分析，利用 2D 超声心动图来区分扩张型心肌病和心脏淀粉样变。该方法通过将 2D 超声心动图数据分类为五个不同的超声心动图视图，并分别提取每个视图的特征，在疾病分类中结合五个特征。研究结果表明该方法在准确识别扩张型心肌病和心脏淀粉样变方面具有很好的效果。

使用 2D 多视角心脏超声显像区分肥厚型心肌病和心脏淀粉样变性的深度学习驱动流程

A Deep Learning-Driven Pipeline for Differentiating Hypertrophic  Cardiomyopathy from Cardiac Amyloidosis Using 2D Multi-View Echocardiography

Traditional deep learning approaches for breast cancer classification has
predominantly concentrated on single-view analysis. In clinical practice,
however, radiologists concurrently examine all views within a mammography exam,
leveraging the inherent correlations in these views to effectively detect
tumors. Acknowledging the significance of multi-view analysis, some studies
have introduced methods that independently process mammogram views, either
through distinct convolutional branches or simple fusion strategies,
inadvertently leading to a loss of crucial inter-view correlations. In this
paper, we propose an innovative multi-view network exclusively based on
transformers to address challenges in mammographic image classification. Our
approach introduces a novel shifted window-based dynamic attention block,
facilitating the effective integration of multi-view information and promoting
the coherent transfer of this information between views at the spatial feature
map level. Furthermore, we conduct a comprehensive comparative analysis of the
performance and effectiveness of transformer-based models under diverse
settings, employing the CBIS-DDSM and Vin-Dr Mammo datasets. Our code is
publicly available at this https URL

本文提出了一种创新的基于 Transformer 的多视图网络，用于解决乳房 X 线照片分类中的挑战。我们的方法通过引入一种新颖的基于移动窗口的动态注意力块，促进多视图信息的有效集成，并在空间特征映射级别上促进该信息在视图之间的一致传递。此外，我们使用 CBIS-DDSM 和 Vin-Dr Mammo 数据集对基于 Transformer 的模型在不同设置下的性能和有效性进行了全面的比较分析。我们的代码公开可用于此 https URL。