Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification

2026-01-27

AI Summary

This study addresses the high inter-observer variability in manual assessment of left ventricular ejection fraction (LVEF) from echocardiograms, as well as the substantial computational cost and limited interpretability of existing deep learning models. To this end, the authors propose a novel multi-task green learning framework that eliminates the need for backpropagation, marking the first application of green learning to cardiac ultrasound analysis. The approach integrates an unsupervised VoxelHop spatiotemporal feature encoder, a multi-level regression decoder, and an XGBoost classifier to simultaneously perform left ventricular segmentation and LVEF classification. Evaluated on the EchoNet-Dynamic dataset, the model achieves a classification accuracy of 94.3% and a Dice coefficient of 0.912, outperforming several state-of-the-art 3D deep learning methods while reducing parameter count by over an order of magnitude—demonstrating superior accuracy, computational efficiency, and interpretability.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Echocardiography is a cornerstone for managing heart failure (HF), with Left Ventricular Ejection Fraction (LVEF) being a critical metric for guiding therapy. However, manual LVEF assessment suffers from high inter-observer variability, while existing Deep Learning (DL) models are often computationally intensive and data-hungry"black boxes"that impede clinical trust and adoption. Here, we propose a backpropagation-free multi-task Green Learning (MTGL) framework that performs simultaneous Left Ventricle (LV) segmentation and LVEF classification. Our framework integrates an unsupervised VoxelHop encoder for hierarchical spatio-temporal feature extraction with a multi-level regression decoder and an XG-Boost classifier. On the EchoNet-Dynamic dataset, our MTGL model achieves state-of-the-art classification and segmentation performance, attaining a classification accuracy of 94.3% and a Dice Similarity Coefficient (DSC) of 0.912, significantly outperforming several advanced 3D DL models. Crucially, our model achieves this with over an order of magnitude fewer parameters, demonstrating exceptional computational efficiency. This work demonstrates that the GL paradigm can deliver highly accurate, efficient, and interpretable solutions for complex medical image analysis, paving the way for more sustainable and trustworthy artificial intelligence in clinical practice.

Problem

• echocardiography
• Left Ventricular Ejection Fraction
• deep learning interpretability
• computational efficiency
• multi-task learning

Innovation

• Green Learning
• backpropagation-free
• multi-task learning
• interpretable AI
• echocardiographic segmentation