A Multiscale Attention-Based Deep Learning Method for DCE-MRI Breast Tumor Segmentation

Breast cancer, the most diagnosed cancer among women, demands accurate diagnosis for effective treatment. Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) provides detailed spatial insights into tissue characteristics, making it essential for tumor analysis. However, segmenting circumscribed mass and diffuse non-mass lesions remains a challenge, as existing solutions often rely on single-context images or multiscale approaches that overlook the imaged structural organization of breast anatomy. To address these gaps, we propose VENUS, a multiscale image and feature attention-based network for breast tumor segmentation in DCE-MRI. Inspired by physicians’ image inspection routine, it utilizes a multiscale encoder with Convolutional Feature Fusion Blocks utilizing early fusion to combine full-breast views with detailed single-breast zoom-ins and improve cross-context semantic modeling ability. A novel attention-based decoder with Attention Gating enhances skip connections by prioritizing critical features for accurate reconstruction. Experiments reveal significant performance gains of 11.05% and 15.07% Dice Similarity Coefficient over single-context state-of-the-art methods on clinical and public DCE-MRI datasets, respectively.