  Standard classification methods based on handcrafted morphological and
texture features have achieved good performance in breast mass differentiation
in ultrasound (US). In comparison to deep neural networks, commonly perceived
as "black-box" models, classical techniques are based on features that have
well-understood medical and physical interpretation. However, classifiers based
on morphological features commonly underperform in the presence of the
shadowing artifact and ill-defined mass borders, while texture based
classifiers may fail when the US image is too noisy. Therefore, in practice it
would be beneficial to select the classification method based on the appearance
of the particular US image. In this work, we develop a deep meta-network that
can automatically process input breast mass US images and recommend whether to
apply the shape or texture based classifier for the breast mass
differentiation. Our preliminary results demonstrate that meta-learning
techniques can be used to improve the performance of the standard classifiers
based on handcrafted features. With the proposed meta-learning based approach,
we achieved the area under the receiver operating characteristic curve of 0.95
and accuracy of 0.91.
