  Deep learning (DL) can fail when there are data mismatches between training
and testing data. Due to its operator-dependent nature, acquisition-related
data mismatches, caused by different scanner settings, can occur in ultrasound
imaging. Therefore, mitigating effects of such data mismatches is essential for
wider clinical adoption of DL powered ultrasound imaging. To mitigate the
effects, ideally we need to collect a large training set at each scanner
setting. However, acquiring such training sets is expensive. Another approach
could be training on a subset of imaging settings, which makes the data
generation less expensive. However, there will still be generalization issues.
As an alternative approach that is inexpensive and generalizable, we propose to
collect a large training set at a single setting and a small calibration set at
each scanner setting. Then, the calibration set will be used to calibrate data
mismatches by using a signals and systems perspective. We tested the proposed
solution to classify two phantoms. To investigate generalizability of the
proposed solution, we calibrated three types of data mismatches: pulse
frequency, focus and output power mismatches. To calibrate the setting
mismatches, we calculated the setting transfer functions. The CNN trained with
no calibration resulted in mean classification accuracies of 55.3%, 64.4% and
70.3% for pulse frequency, focus and output power mismatches, respectively. By
using the setting transfer functions, which allowed a matching of the training
and testing domains, we obtained mean accuracies of 95.3%, 92.99% and 99.32%,
respectively. Therefore, the incorporation of the setting transfer functions
between scanner settings can provide an economical means of generalizing a DL
model for specific classification tasks where scanner settings are not fixed by
the operator.
