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.

翻译：摘要：大多数通用目标检测器主要针对COCO和PASCAL VOC等标准目标检测任务构建。对于由与标准数据集视觉差异较大的图像构成的其他领域任务，它们可能无法高效或有效地工作。为此，许多研究致力于通过有限的领域特定设计来适配通用目标检测器。然而，设计成功的任务专用检测器需要额外的人工实验和反复试错的参数调整。本文首次提出并检验了一种全自动流水线，用于设计全专用检测器（FSD），该方法主要通过探索骨干网络与任务专用头部之间的理想网络结构，整合了神经架构搜索模型。在DeepLesion数据集上的大量实验结果表明，在二分类病灶检测任务中，FSD可在参数减少约40%的情况下实现3.1个mAP的提升；在多类型病灶检测任务中，通过我们提出的区域感知图建模，相较于现有通用医学病灶检测网络，mAP提升约10%。