# nnDetection: A Self-configuring Method for Medical Object Detection

arXiv: https://arxiv.org/abs/2106.00817 · HTML: https://mlsanity.com/paper/2106.00817/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge](https://mlsanity.com/paper/1612.08012/) (2016)
- [The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes](https://mlsanity.com/paper/1904.00445/) (2019)
- [Why rankings of biomedical image analysis competitions should be interpreted with care](https://mlsanity.com/paper/1806.02051/) (2018)
- [A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations](https://mlsanity.com/paper/1406.2639/) (2014)
- [Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection](https://mlsanity.com/paper/1811.08661/) (2018)
- [DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification](https://mlsanity.com/paper/1801.09555/) (2018)
- [Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels](https://mlsanity.com/paper/1906.02283/) (2019)
- [Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1706.04303/) (2017)
- [S4ND: Single-Shot Single-Scale Lung Nodule Detection](https://mlsanity.com/paper/1805.02279/) (2018)
- [3DFPN-HS2: 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection](https://mlsanity.com/paper/1906.03467/) (2019)
- [Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning](https://mlsanity.com/paper/1708.03867/) (2017)

## Built on by (most cited first, 30 of 60)

- [MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow](https://mlsanity.com/paper/2503.18968/) (2025)
- [The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography](https://mlsanity.com/paper/2312.17670/) (2023)
- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning](https://mlsanity.com/paper/2402.02045/) (2024)
- [Diffeomorphic Image Registration with Neural Velocity Field](https://mlsanity.com/paper/2202.12498/) (2022)
- [SATr: Slice Attention with Transformer for Universal Lesion Detection](https://mlsanity.com/paper/2203.07373/) (2022)
- [medigan: a Python library of pretrained generative models for medical image synthesis](https://mlsanity.com/paper/2209.14472/) (2022)
- [Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI](https://mlsanity.com/paper/2112.05151/) (2021)
- [Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection](https://mlsanity.com/paper/2205.00122/) (2022)
- [A Learning-based Framework for Topology-Preserving Segmentation using Quasiconformal Mappings](https://mlsanity.com/paper/2210.03299/) (2022)
- [Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography](https://mlsanity.com/paper/2111.15409/) (2021)
- [Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting](https://mlsanity.com/paper/2409.13416/) (2024)
- [Focused Decoding Enables 3D Anatomical Detection by Transformers](https://mlsanity.com/paper/2207.10774/) (2022)
- [MedYOLO: A Medical Image Object Detection Framework](https://mlsanity.com/paper/2312.07729/) (2023)
- [Topology-Preserving Segmentation Network: A Deep Learning Segmentation Framework for Connected Component](https://mlsanity.com/paper/2202.13331/) (2022)
- [Taming Detection Transformers for Medical Object Detection](https://mlsanity.com/paper/2306.15472/) (2023)
- [Anatomy-Aware Lymph Node Detection in Chest CT using Implicit Station Stratification](https://mlsanity.com/paper/2307.15271/) (2023)
- [Voxel Scene Graph for Intracranial Hemorrhage](https://mlsanity.com/paper/2407.21580/) (2024)
- [Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario](https://mlsanity.com/paper/2411.02477/) (2024)
- [Robust Detection Outcome: A Metric for Pathology Detection in Medical Images](https://mlsanity.com/paper/2303.01920/) (2023)
- [Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer](https://mlsanity.com/paper/2404.03819/) (2024)
- [CAF-YOLO: A Robust Framework for Multi-Scale Lesion Detection in Biomedical Imagery](https://mlsanity.com/paper/2408.01897/) (2024)
- [Anatomical Positional Embeddings](https://mlsanity.com/paper/2409.10291/) (2024)
- [MixMicrobleedNet: segmentation of cerebral microbleeds using nnU-Net](https://mlsanity.com/paper/2108.01389/) (2021)
- [nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods](https://mlsanity.com/paper/2209.01124/) (2022)
- [Detection of Intracranial Hemorrhage for Trauma Patients](https://mlsanity.com/paper/2408.10768/) (2024)
- [Mind the Detail: Uncovering Clinically Relevant Image Details in Accelerated MRI with Semantically Diverse Reconstructions](https://mlsanity.com/paper/2507.00670/) (2025)
- [Evaluating Generic Auto-ML Tools for Computational Pathology](https://mlsanity.com/paper/2112.03622/) (2021)
- [Segmentation-Consistent Probabilistic Lesion Counting](https://mlsanity.com/paper/2204.05276/) (2022)
- [Multi-modal volumetric concept activation to explain detection and classification of metastatic prostate cancer on PSMA-PET/CT](https://mlsanity.com/paper/2208.02555/) (2022)

## Abstract (arXiv)

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 .