Deep classification of breast cancer in ultrasound images: more classes, better results with multi-task learning

Ultrasound (US) is a low-cost, portable, and safe tool for breast cancer screening. However, automatic classification of invasive ductal carcinoma (IDC) in US is a difficult classification task due to their similar appearance to fibroadenoma (FA) (a type of benign tumor). Another challenge is the limited availability of US data with ground truth labels, further complicating the adoption of deep learning techniques for IDC detection. It has been shown that deep classification networks perform better when they simultaneously learn multiple correlated tasks. However, most previous studies on breast US classifications focused on the binary classification of benign versus malignant tumors. To this end, we propose a multi-class classification deep learning-based strategy mainly focusing on the classification of IDC. Inspired by multi-task learning (MTL), we adopt a novel scheme in adding the background tissue as an additional class and show substantial improvements in IDC detection.