Xiao-Xia Yina
Guangzhou University, China
Title: Updated loss function for accurate tumor segmentation from breast MRIs
Biography
Biography: Xiao-Xia Yina
Abstract
We present Focal Boundary Dice, a new segmentation evaluation measure focused on boundary quality and class imbalance. We perform an extensive analysis across different error types and object sizes of imaged tumors from MRI scan and show that Focal Boundary Dice is significantly more sensitive than the standard Focal and Dice measures to boundary errors for imaged tumors from MRI scans and does not over-penalize errors on division of the boundary, including smaller imaged objects. The new quality measure displays several desirable characteristics, like higher accuracy in the selection of hard samples, prediction/ground truth pairs, and balanced responsiveness across scales, which makes it more suitable for segmentation evaluation than other classification focused measures such as combined IoU and BCE loss, Boundary BCE loss and Shape-aware Loss.