This is the source code for the paper, "RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection" accepted by the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), of which I am the first author. The paper is available to read on the conference proceedings and download from Springer or arXiv.

The model configuration (i.e., network construction) file is rcs-yolo.yaml (2 heads) or rcs3-yolo.yaml (3 heads) in the directory ./cfg/training/. The RepVGG/RepConv ShuffleNet based One-Shot Aggregation (RCS-OSA) module file is rcsosa.py in the directory ./models/, which is the unique module we proposed.

Recommended dependencies:

Python <= 3.8
Torch <= 1.7.1
CUDA <= 11.1

The hyperparameter setting file is hyp_training.yaml in the directory ./data/.

python train.py --workers 8 --device 0 --batch-size 32 --data data/br35h.yaml --img 640 640 --cfg cfg/training/rcs-yolo.yaml --weights '' --name rcs-yolo --hyp data/hyp_training.yaml

python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch-size 128 --data data/br35h.yaml --img 640 640 --cfg cfg/training/rcs-yolo.yaml --weights '' --name rcs-yolo --hyp data/hyp_training.yaml

The model weights we pretrained on the brain tumor detection was saved as best.pt in the directory ./runs/train/exp/weights/.

python test.py --data data/br35h.yaml --img 640 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights runs/train/exp/weights/best.pt --name val

We trained and evaluated RCS-YOLO on the dataset Br35H :: Brain Tumor Detection 2020. The .txt format annotations in the folder dataset-Br35H are coverted from original json format. We used 500 images of which in the ’traindata’ folder were selected as the training set, while the other 201 images in the ’valdata’ folder as the testing set.

The screenshots of evaluation results were saved in the directory ./runs/val/.

The files train_repvgg-csp.py and https://test_repvgg-csp.py are used for an ablation study of the comparison network repvgg-csp.yaml in the directory ./cfg/ablation/.

Please cite our paper if you use code from this repository:

Plain Text

• Springer Style
  Kang, M., Ting, C.-M., Ting, F.F., Phan, R.C.-W.: RCS-YOLO: a fast and high-accuracy object detector for brain tumor detection. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14223, 600–610. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-43901-8_57
  ^{NOTE: MICCAI conference proceedings are part of the book series LNCS in which Springer's format for bibliographical references is strictly enforced. This is important, for instance, when citing previous MICCAI proceedings. LNCS stands for Lecture Notes in Computer Science.}

• Nature Style
  Kang, M., Ting, C.-M., Ting, F. F. & Phan, R. C.-W. RCS-YOLO: a fast and high-accuracy object detector for brain tumor detection. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, Canada, October 8–12, 2023, Proceedings, Part IV (eds. Greenspan, H. et al.) 600–610 (Springer, 2023).
  

• IEEE Style
  M. Kang, C.-M. Ting, F. F. Ting, and R. C.-W. Phan, "Rcs-yolo: A fast and high-accuracy object detector for brain tumor detection," in Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv. (MICCAI), Vancouver, BC, Canada, Oct. 8–12, 2023, 600–610.
  ^{NOTE: City of Conf., Abbrev. State, Country, Month & day(s) are optional.}

BibTeX Format

\begin{thebibliography}{1}
\bibitem{Kang23Rcsyolo} Kang, M., Ting, C.-M., Ting, F.F., Phan, R.C.-W.: RCS-YOLO: a fast and high-accuracy object detector for brain tumor detection. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14223, 600--610. Springer, Cham (2023). {\UrlFont https://doi.org/10.1007/978-3-031-43901-8\_57}
\end{thebibliography}

@inproceedings{Kang23Rcsyolo,
  author = "Kang, Ming and Ting, Chee-Ming and Ting, Fung Fung and Phan, Rapha{\"e}l C.-W.",
  title = "RCS-YOLO: a fast and high-accuracy object detector for brain tumor detection",
  editor = "Greenspan, Hayit and et al.",
  booktitle = "Medical Image Computing and Computer-Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, Canada, October 8--12, 2023, Proceedings, Part ?",
  series = "Lecture Notes in Computer Science (LNCS)",
  volume = "14223",
  pages = "600--610",
  publisher = "Springer",
  address = "Cham",
  year = "2023",
  note = "in press",
  doi= "10.1007/978-3-031-43901-8_57",
  url = "https://doi.org/10.1007/978-3-031-43901-8_57"
}

@inproceedings{Kang23Rcsyolo,
  author = "Ming Kang and Chee-Ming Ting and Fung Fung Ting and Rapha{\"e}l C.-W. Phan",
  title = "Rcs-yolo: A fast and high-accuracy object detector for brain tumor detection",
  booktitle = "Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv. (MICCAI)",
  address = "Vancouver, BC, Canada, Oct. 8--12",
  pages = "600--610",
  year = "2023",
}

^{NOTE: Please remove some optional BibTeX fields, for example, series, volume, address, url and so on, while the LaTeX compiler produces an error. Author names may be manually modified if not automatically abbreviated by the compiler under the control of the .bst file. kang2023rcsyolo could be b1, bib1, or ref1 when references appear in the order in which they are cited. The quotation mark pair "" in the field could be replaced by the brace {}.}

RCS-YOLO is released under the GNU General Public License v3.0. Please see the LICENSE file for more information.

Many utility codes of our project base on the codes of YOLOv7, RepVGG, ShuffleNet, and VoVNetV2 repositories.