  Image classification is widely used to build predictive models for breast
cancer diagnosis. Most existing approaches overwhelmingly rely on deep
convolutional networks to build such diagnosis pipelines. These model
architectures, although remarkable in performance, are black-box systems that
provide minimal insight into the inner logic behind their predictions. This is
a major drawback as the explainability of prediction is vital for applications
such as cancer diagnosis. In this paper, we address this issue by proposing an
explainable machine learning pipeline for breast cancer diagnosis based on
ultrasound images. We extract first- and second-order texture features of the
ultrasound images and use them to build a probabilistic ensemble of decision
tree classifiers. Each decision tree learns to classify the input ultrasound
image by learning a set of robust decision thresholds for texture features of
the image. The decision path of the model predictions can then be interpreted
by decomposing the learned decision trees. Our results show that our proposed
framework achieves high predictive performance while being explainable.
