Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels

Understand

Accurate, automated lesion detection in Computed Tomography (CT) is an important yet challenging task due to the large variation of lesion types, sizes, locations and appearances.

• Recent work on CT lesion detection employs two-stage region proposal based methods trained with centroid or bounding-box annotations.
• We propose a highly accurate and efficient one-stage lesion detector, by re-designing a RetinaNet to meet the particular challenges in medical imaging.
• Specifically, we optimize the anchor configurations using a differential evolution search algorithm.