EGE-UNet This is the official code repository for "EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation", which is accpeted by26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2023)as a regular paper!
论文:《EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation》 代码仓库:github.dev/JCruan519/EG 首先看模型整体结构图: 上图红圈里说了,MP代表Max Pooling,BI代表Bilinear Interpolation。至于图中的GHPA和GAB是什么,后面会讲。 相关代码链接。 由下面的代码截图可见,Max Pooling的核大小为...
Our code is available at https://github.com/JCruan519/EGE-UNet .Ruan, JiachengShanghai Jiao Tong UniversityXie, MingyeShanghai Jiao Tong UniversityGao, JingshengShanghai Jiao Tong UniversityLiu, TingShanghai Jiao Tong UniversityFu, Yuzhuo
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To address this issue, we propose a more efficient approach, the Efficient Group Enhanced UNet ( EGE-UNet ). We incorporate a Group multi-axis Hadamard Product Attention module (GHPA) and a Group Aggregation Bridge module (GAB) in a lightweight manner. The GHPA groups input features and ...
│ │ ├── Unet <- Unet models for image examples | ├── .gitignore <- List of files ignored by git ├── .pre-commit-config.yaml <- Configuration of pre-commit hooks for code formatting ├── pyproject.toml <- Configuration options for testing and linting ...
This is the official code repository for "EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation", which is accpeted by 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2023) as a regular paper! 0. Main Environments python 3.8 pytor...
(V0) of the library │ |── torchcfm <- Code base of our Flow Matching methods | ├── conditional_flow_matching.py <- CFM classes │ ├── models <- Model architectures │ │ ├── models <- Models for 2D examples │ │ ├── Unet <- Unet models for image examples | ├...