nnDetection: A Self-configuring Method for Medical Object Detection

TL;DR

nnDetection is an automated, self-configuring method for medical object detection that adapts to various problems without manual tuning, achieving state-of-the-art results on multiple benchmarks.

Contribution

It extends the nnU-Net framework to medical object detection, providing a fully automated configuration process for diverse detection tasks.

Findings

Achieves competitive or superior results compared to existing methods.

Successfully applied to multiple public benchmarks.

Proposes 11 new detection tasks for comprehensive evaluation.

Abstract

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 .