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CPMNetv2: A Simpler and Stronger 3D Object Detection Benchmark in Medical Image

Installation

Create conda env

source build_env.sh

Train

bash train_xxx.sh

Note: args.num_sam depend on the average number of instance (lesion) in per sample (N), suggest you set to 2N. The real batch size is (args.batch_size * args.num_sam), be careful with your GPU memory.

If you use CPMNetv2, please cite our papers:

{@inproceedings{song2020cpm,
title={CPM-Net: A 3D Center-Points Matching Network for Pulmonary Nodule Detection in CT Scans},
author={Song, Tao and Chen, Jieneng and Luo, Xiangde and Huang, Yechong and Liu, Xinglong and Huang, Ning and Chen, Yinan and Ye, Zhaoxiang and Sheng, Huaqiang and Zhang, Shaoting and others},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={550--559},
year={2020},
organization={Springer}
}

@article{luo2021scpmnet,
title={SCPM-Net: An anchor-free 3D lung nodule detection network using sphere representation and center points matching},
author={Luo, Xiangde and Song, Tao and Wang, Guotai and Chen, Jieneng and Chen, Yinan and Li, Kang and Metaxas, Dimitris N and Zhang, Shaoting},
journal={Medical Image Analysis},
volume={75},
pages={102287},
year={2022},
publisher={Elsevier}
}

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