  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.
