Explaining a deep learning based breast ultrasound image classifier with saliency maps

Abstract

Deep neural networks have achieved good performance in breast mass classification in ultrasound imaging. However, their usage in clinical practice is still limited due to the lack of explainability of decisions conducted by the networks. In this study, to address the explainability problem, we generated saliency maps indicating ultrasound image regions important for the network’s classification decisions.

Keywords

• deep learning
• breast mass diagnosis
• attention maps
• explainability