@@ -16,7 +16,7 @@ Following nnU-Net’s agenda, in this work we systematize and automate the confi
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@@ -16,7 +16,7 @@ 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.
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.
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):
**If you use nnDetection please cite our [paper](https://arxiv.org/abs/2106.00817)**:
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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
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
