Breast Cancer Detection on Dual-View Sonography via Data-Centric Deep Learning.

This study aims to enhance AI-assisted breast cancer diagnosis through dual-view sonography using a data-centric approach. We customize a DenseNet-based model on our exclusive dual-view breast ultrasound dataset to enhance the model's ability to differentiate between malignant and benign masses. Various assembly strategies are designed to integrate the dual views into the model input, contrasting with the use of single views alone, with a goal to maximize performance.