基于自监督卷积核的人工特征协调：超声心动图上增强的左心室肥厚疾病表型分析
Self supervised convolutional kernel based handcrafted feature harmonization: Enhanced left ventricle hypertension disease phenotyping on echocardiography
AI总结：
本研究提出一种基于自监督卷积核的预处理方法，将图像转换为特征图以实现手工特征协调，在超声心动图左心室肥厚疾病诊断中展现出更优的分类性能。
摘要：
影像组学作为一种医学成像技术，从图像中提取定量的手工特征以预测疾病。这些特征的协调确保了在不同成像设备和协议下特征提取的一致性。协调方法包括标准化成像协议、统计调整以及评估特征鲁棒性。诸如左心室肥厚（LVH）和高血压性心脏病（HHD）等心肌疾病通过超声心动图进行诊断，但多变的成像设置带来了挑战。在此场景下，协调技术对于将手工特征应用于疾病诊断至关重要。自监督学习（SSL）增强了有限数据集内的数据理解，并适应多样的数据设置。ConvNeXt-V2将卷积层集成到SSL中，在各种任务中展现出卓越性能。本研究聚焦于SSL中的卷积滤波器，将其用作预处理把图像转换为特征图，以进行手工特征协调。我们提出的方法在协调评估中表现优异，与现有方法相比，展现出更优的LVH分类性能。
英文摘要：
Radiomics, a medical imaging technique, extracts quantitative handcrafted features from images to predict diseases. Harmonization in those features ensures consistent feature extraction across various imaging devices and protocols. Methods for harmonization include standardized imaging protocols, statistical adjustments, and evaluating feature robustness. Myocardial diseases such as Left Ventricular Hypertrophy (LVH) and Hypertensive Heart Disease (HHD) are diagnosed via echocardiography, but variable imaging settings pose challenges. Harmonization techniques are crucial for applying handcrafted features in disease diagnosis in such scenario. Self-supervised learning (SSL) enhances data understanding within limited datasets and adapts to diverse data settings. ConvNeXt-V2 integrates convolutional layers into SSL, displaying superior performance in various tasks. This study focuses on convolutional filters within SSL, using them as preprocessing to convert images into feature maps for handcrafted feature harmonization. Our proposed method excelled in harmonization evaluation and exhibited superior LVH classification performance compared to existing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
