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

Explainable Ensemble Machine Learning for Breast Cancer Diagnosis based on Ultrasound Image Texture Features