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(MICCAI 2021) Automatic Polyp Segmentation via Multi-scale Subtraction Network

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MSNet&M2SNet

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MSNet: Automatic Polyp Segmentation via Multi-scale Subtraction Network

Xiaoqi Zhao, Lihe Zhang, Huchuan Lu
⭐ arXiv »

M2SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation

Xiaoqi Zhao, Hongpeng Jia, Youwei Pang, Long Lv, Feng Tian, Lihe Zhang, Weibing Sun, Huchuan Lu
⭐ arXiv »


Datasets

Results

Trained Model

Highlight

Novel Segmentation Architectures


Efficient Intra-Layer Multi-scale Subtraction Design



Efficient Inter-Layer Multi-scale Subtraction Structure


Training-free Loss Network


Low FLOPs (comparisons under the Res2Net-50 backbone)


Prerequisites

Training/Inference/Testing

  • set the cfg in train.py:
    Dataset.Config(datapath='', savepath='', mode='train', batch=16, lr=0.05, momen=0.9, decay=5e-4, epoch='')
    %the number of training epochs settings in the polyp segmentation, COVID-19 Lung Infection, breast tumor segmentation and OCT layer segmentation are 50, 200, 100         and 100, respectively.
    python train.py
  • Run prediction_rgb.py (can generate the predicted maps)
  • Run test_score.py (support 10 binary segmentation evaluation metrics: MAE, maxF, avgF, wfm, Sm, Em, M_dice, M_iou, Ber, Acc)

TODO LIST

  • 3D verison MSNet training.

  • Support different backbones (VGGNet, MobileNet, ResNet, Swin, etc.).

  • Diverse Medical Image Segmentation

    • Polyp
    • COVID-19 Lung Infection
    • Breast tumor
    • OCT Layer
    • Prostate
    • Cell Nuclei
    • Liver
    • Retinal Vessel
    • Skin Lesion
    • Lung
    • Pancreas
    • Hippocampus
    • Heart
    • BrainTumour

BibTex

@inproceedings{MSNet,
  title={Automatic polyp segmentation via multi-scale subtraction network},
  author={Zhao, Xiaoqi and Zhang, Lihe and Lu, Huchuan},
  booktitle={MICCAI},
  pages={120--130},
  year={2021},
  organization={Springer}
}
@article{M2SNet,
  title={M $\^{}$\{$2$\}$ $ SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation},
  author={Zhao, Xiaoqi and Jia, Hongpeng and Pang, Youwei and Lv, Long and Tian, Feng and Zhang, Lihe and Sun, Weibing and Lu, Huchuan},
  journal={arXiv preprint arXiv:2303.10894},
  year={2023}
}

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