  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.
