Awesome
MSNet&M2SNet
<p align="center"> <img src="./image/logo.png" alt="Logo" width="150" height="auto"> <h3 align="center">MSNet: Automatic Polyp Segmentation via Multi-scale Subtraction Network</h3> <p align="center"> Xiaoqi Zhao, Lihe Zhang, Huchuan Lu <br /> <a href="https://arxiv.org/pdf/2108.05082.pdf"><strong>⭐ arXiv »</strong></a> <br /> </p> <h3 align="center">M2SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation</h3> <p align="center"> Xiaoqi Zhao, Hongpeng Jia, Youwei Pang, Long Lv, Feng Tian, Lihe Zhang, Weibing Sun, Huchuan Lu <br /> <a href="https://arxiv.org/pdf/2303.10894.pdf"><strong>⭐ arXiv »</strong></a> <br /> </p> </p> <p align="center"> <img src="./image/MICCAI_2022_GOALS_award.jpg"/> <br /> </p>- Official repository of "Automatic Polyp Segmentation via Multi-scale Subtraction Network" MICCAI-2021.
- Official repository of "M2SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation".
- :boom: We won the second place (2/100) in the MICCAI 2022 Challenge: Glaucoma Oct Analysis and Layer Segmentation (GOALS).
Datasets
- Image Polyp Segmentation: (training dataset) Google Drive / BaiduYunPan(342v); (testing dataset) Google Drive / BaiduYunPan(4e10)
- Video Polyp Segmentation: (training dataset) Google Drive / BaiduYunPan(4b45); (testing dataset) Google Drive / BaiduYunPan(ki4a)
- COVID-19 Lung Infection: (training dataset) Google Drive / BaiduYunPan(uoiy); (testing dataset) Google Drive / BaiduYunPan(r867)
- Breast Ultrasound Segmentation: Google Drive / BaiduYunPan(409p)
Results
- MSNet: Google Drive / BaiduYunPan(j3i8)
- M2SNet: Google Drive / BaiduYunPan(a60m)
Trained Model
- You can download the trained MSNet model at Google Drive / BaiduYunPan(ps6e).
- You can download the trained M2SNet model at Google Drive / BaiduYunPan(5vo3).
- You can download Res2Net weights at Google Drive / BaiduYunPan(w46l)
Highlight
Novel Segmentation Architectures
<p align="center"> <img src="./image/network_structure_compare.png"/> <br /> </p>Efficient Intra-Layer Multi-scale Subtraction Design
<p align="center"> <img src="./image/intra_layer_mssu.png"/> <br /> </p> <p align="center"> <img src="./image/intra-layer-ms-compare.png"/> <br /> </p>Efficient Inter-Layer Multi-scale Subtraction Structure
<p align="center"> <img src="./image/inter_layer_ms.png"/> <br /> </p>Training-free Loss Network
<p align="center"> <img src="./image/lossnet.png"/> <br /> </p>Low FLOPs (comparisons under the Res2Net-50 backbone)
<p align="center"> <img src="./image/Flops_compare.png"/> <br /> </p>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}
}