@@ -17,6 +17,12 @@ Following nnU-Net’s agenda, in this work we systematize and automate the confi
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 public data sets for a comprehensive evaluation of medical object detection methods.
If you use nnDetection please cite our [paper](https://arxiv.org/abs/2106.00817):
```
Baumgartner, M., Jaeger, P. F., Isensee, F., & Maier-Hein, K. H. (2021). nnDetection: A Self-configuring Method for Medical Object Detection. arXiv preprint arXiv:2106.00817
```
:tada: nnDetection was early accepted to the International Conference on Medical Image Computing & Computer Assisted Intervention 2021 (MICCAI21) :tada:
# Installation
## Docker
The easiest way to get started with nnDetection is the provided is to build a Docker Container with the provided Dockerfile.
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@@ -449,12 +455,6 @@ We are planning to provide prebuild wheels in the future but no prebuild wheels
Please use the provided Dockerfile or the installation instructions to run nnDetection.
</details>
# Cite
If you use nnDetection for your project/research/work please cite the following paper:
```text
Coming Soon
```
# Acknowledgements
nnDetection combines the information from multiple open source repositores we wish to acknoledge for their awesome work, please check them out!
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@@ -466,3 +466,6 @@ The Medical Detection Toolkit introduced the first codebase for 3D Object Detect
nnDetection tried to follow the interfaces of torchvision to make it easy to understand for everyone coming from the 2D (and video) detection scene. As a result we used based our implementations of some of the core modules of the torchvision implementation.
## Funding
Part of this work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 410981386 and the Helmholtz Imaging Platform (HIP), a platform of the Helmholtz Incubator on Information and Data Science.
