Awesome
Some codes for our experiments, you can use and change it by youself
Suport Method
-
DeepLabV3: Rethinking Atrous Convolution for Semantic Image Segmentation
-
DeepLabV3 Plus: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
-
ESPNet V2: ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
-
UNet: U-Net: Convolutional Networks for Biomedical Image Segmentation
-
PSPNet: Pyramid Scene Parsing Network
-
STDCNet: Rethinking BiSeNet For Real-time Semantic Segmentation
-
SCS-Net: SCS-Net: A Scale and Context Sensitive Network for Retinal Vessel Segmentation
-
SA-UNet: SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
-
BiSeNetV2: BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation
-
D2SF: Rethinking Dual-Stream Super-Resolution Semantic Learning in Medical Image Segmentation Official
-
SuperVessel: SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image Official
-
SS-MAF Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module Offficial
Usage
-
Clone or download this repository to your computer
git clone https://github.com/Qsingle/imed_vision.git
-
Follow the samples at the
configs
directory to set the parameters used to train. -
Run the training script (e.g. vessel_segmentation_train.py).
python scripts/vessel_segmentation_train.py --json_path ./configs/iostar_vessel_segmentation.json
TODO
- Update the structure for this repository
- Add the scripts for classification model train
Acknowledgment
Cititions
@inproceedings{hu2022supervessel,
title={Supervessel: Segmenting high-resolution vessel from low-resolution retinal image},
author={Hu, Yan and Qiu, Zhongxi and Zeng, Dan and Jiang, Li and Lin, Chen and Liu, Jiang},
booktitle={Chinese Conference on Pattern Recognition and Computer Vision (PRCV)},
pages={178--190},
year={2022},
organization={Springer Nature Switzerland Cham}
}
@inproceedings{zhang2022hard,
title={Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module},
author={Zhang, Jiayi and Chen, Xiaoshan and Qiu, Zhongxi and Yang, Mingming and Hu, Yan and Liu, Jiang},
booktitle={2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
pages={1375--1380},
year={2022},
organization={IEEE}
}
@article{qiu2023learnable,
title={Learnable ophthalmology sam},
author={Qiu, Zhongxi and Hu, Yan and Li, Heng and Liu, Jiang},
journal={arXiv preprint arXiv:2304.13425},
year={2023}
}
@article{qiu2023rethinking,
title={Rethinking Dual-Stream Super-Resolution Semantic Learning in Medical Image Segmentation},
author={Qiu, Zhongxi and Hu, Yan and Chen, Xiaoshan and Zeng, Dan and Hu, Qingyong and Liu, Jiang},
journal={IEEE Transactions on Pattern Analysis \& Machine Intelligence},
number={01},
pages={1--14},
year={2023},
publisher={IEEE Computer Society}
}