  Simultaneous localisation and categorization of objects in medical images,
also referred to as medical object detection, is of high clinical relevance
because diagnostic decisions often depend on rating of objects rather than e.g.
pixels. For this task, the cumbersome and iterative process of method
configuration constitutes a major research bottleneck. Recently, nnU-Net has
tackled this challenge for the task of image segmentation with great success.
Following nnU-Net's agenda, in this work we systematize and automate the
configuration process for medical object detection. The resulting
self-configuring method, nnDetection, adapts itself without any manual
intervention to arbitrary medical detection problems while achieving results en
par with or superior to the state-of-the-art. We demonstrate the effectiveness
of nnDetection on two public benchmarks, ADAM and LUNA16, and propose 11
further medical object detection tasks on public data sets for comprehensive
method evaluation. Code is at https://github.com/MIC-DKFZ/nnDetection .
