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 10 further medical object detection tasks on public data sets for comprehensive method evaluation. Code is at https://github.com/MIC-DKFZ/nnDetection .

翻译：医疗图象中的物体(也称为医疗物体探测)的同步本地化和分类具有很高的临床相关性,因为诊断决定往往取决于对象的评级,而不是像素等值。关于这项任务,方法配置的繁琐和迭代过程构成一个重大的研究瓶颈。最近,NNU-Net成功地应对了图像分割任务中的这一挑战。在NU-Net的议程完成后,我们在此工作中将医疗物体探测的配置过程系统化和自动化。由此形成的自我配置方法,NNSOD, 在没有任何人工干预的情况下适应任意医疗探测问题,同时取得与最新技术相同或优越的成果。我们展示了在两个公共基准,即ADAM和LUNA16上进行NT的检测的有效性,并提议在公共数据集上进一步执行10项医疗物体探测任务,以进行综合方法评估。代码见https://github.com/MIC-DKFZ/nnSetection。