2023/08/27 by Mohammad Karimzadeh, Aleksandar Vakanski, Karimzadeh, Mohammad +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #AI in cancer detection #Artificial intelligence #BI-RADS #Biomedical Text Mining and Ontologies #Breast cancer #CAD #Cancer #Class (philosophy) #Computer science #Computer-aided diagnosis #Interpretability #Machine learning #Mammography #Medicine #Pattern recognition (psychology) #Radiomics and Machine Learning in Medical Imaging #Segmentation #Task (project management)
paper · pdf · doi:10.48550/arxiv.2308.14213
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.