  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. For training, we leverage the response
evaluation criteria in solid tumors (RECIST) annotation which are measured in
clinical routine. We incorporate dense masks from weak RECIST labels, obtained
automatically using GrabCut, into the training objective, which in combination
with other advancements yields new state-of-the-art performance. We evaluate
our method on the public DeepLesion benchmark, consisting of 32,735 lesions
across the body. Our one-stage detector achieves a sensitivity of 90.77% at 4
false positives per image, significantly outperforming the best reported
methods by over 5%.
