  Most generic object detectors are mainly built for standard object detection
tasks such as COCO and PASCAL VOC. They might not work well and/or efficiently
on tasks of other domains consisting of images that are visually different from
standard datasets. To this end, many advances have been focused on adapting a
general-purposed object detector with limited domain-specific designs. However,
designing a successful task-specific detector requires extraneous manual
experiments and parameter tuning through trial and error. In this paper, we
first propose and examine a fully-automatic pipeline to design a
fully-specialized detector (FSD) which mainly incorporates a
neural-architectural-searched model by exploring ideal network structures over
the backbone and task-specific head. On the DeepLesion dataset, extensive
results show that FSD can achieve 3.1 mAP gain while using approximately 40%
fewer parameters on binary lesion detection task and improved the mAP by around
10% on multi-type lesion detection task via our region-aware graph modeling
compared with existing general-purposed medical lesion detection networks.
