Post-Hoc Explainability of BI-RADS Descriptors in a Multi-task Framework
  for Breast Cancer Detection and Segmentation
Mohammad Karimzadeh Aleksandar Vakanski Min Xian Boyu Zhang
Despite recent medical advancements, breast cancer remains one of the most
prevalent and deadly diseases among women. Although machine learning-based
Computer-Aided Diagnosis (CAD) systems have shown potential to assist
radiologists in analyzing medical images, the opaque nature of the
best-performing CAD systems has raised concerns about their trustworthiness and
interpretability. This paper proposes MT-BI-RADS, a novel explainable deep
learning approach for tumor detection in Breast Ultrasound (BUS) images. The
approach offers three levels of explanations to enable radiologists to
comprehend the decision-making process in predicting tumor malignancy. Firstly,
the proposed model outputs the BI-RADS categories used for BUS image analysis
by radiologists. Secondly, the model employs multi-task learning to
concurrently segment regions in images that correspond to tumors. Thirdly, the
proposed approach outputs quantified contributions of each BI-RADS descriptor
toward predicting the benign or malignant class using post-hoc explanations
with Shapley Values.