A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training

TL;DR

This paper introduces Zone Training, a data-efficient deep learning approach for tissue classification in biomedical ultrasound imaging that reduces training data requirements by dividing the image into zones and training separate networks.

Contribution

The paper proposes Zone Training, a novel method that improves data efficiency by partitioning ultrasound images into zones and training dedicated networks for each, enabling high accuracy with less data.

Findings

Zone Training reduces training data needs by 2-5 times.

It achieves comparable classification accuracy to conventional methods.

Validated on three tissue-mimicking phantoms.

Abstract

Deep learning (DL) powered biomedical ultrasound imaging is an emerging research field where researchers adapt the image analysis capabilities of DL algorithms to biomedical ultrasound imaging settings. A major roadblock to wider adoption of DL powered biomedical ultrasound imaging is that acquiring large and diverse datasets is expensive in clinical settings, which is a requirement for successful DL implementation. Hence, there is a constant need for developing data-efficient DL techniques to turn DL powered biomedical ultrasound imaging into reality. In this work, we develop a data-efficient deep learning training strategy, which we named \textit{Zone Training}. In \textit{Zone Training}, we propose to divide the complete field of view of an ultrasound image into multiple zones associated with different regions of a diffraction pattern and then, train separate DL networks for each zone. The main advantage of \textit{Zone Training} is that it requires less training data to achieve high accuracy. In this work, three different tissue-mimicking phantoms were classified by a DL network. The results demonstrated that \textit{Zone Training} required a factor of 2-5 less training data to achieve similar classification accuracies compared to a conventional training strategy.