Self supervised convolutional kernel based handcrafted feature harmonization: Enhanced left ventricle hypertension disease phenotyping on echocardiography

TL;DR

This paper introduces a self-supervised convolutional kernel approach to harmonize handcrafted features in echocardiography, significantly improving hypertrophy disease phenotyping accuracy across different imaging settings.

Contribution

It presents a novel method combining convolutional kernels with self-supervised learning for feature harmonization in echocardiography, enhancing disease classification performance.

Findings

Superior harmonization performance demonstrated

Improved LVH classification accuracy

Effective adaptation to diverse imaging protocols

Abstract

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.