DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmen_dcunet代码

1 Main Contribution

1. Designed efficient CNN architecture to replace encoder and decoder
2. applied residual module to replace skip connection between encoder and decoder to improve based on state-of-art U-Net model

2 Datasets

• In-house IR breast dataset contains 450 infrared images from 14 patients and 16 healthy volunteers； resolution is 256*128
• EMdataset is 2D EM(电子显微镜图像，公有)：contains 30 images in its training set from a serial section Transmission Electron Microscopy （ssTEM） of the Drosophila(果蝇幼虫EM图像)， resolution is 256*256； Use 5-Fold cross-validation
• Endoscopy images(内窥镜图像 公有): CVC-ClinicDB dataset extracte from the colonoscopy(结肠镜检查)， contains total 612 images; resolution is 128*96

3 Data Augment

1. convert 16-bit to 8-bit and resize 256*128 for thermography breast database
2. resize 256*256 for other databases

4 Initialization

1. All convolutional layers are activated by ReLU
2. Each ReLU later use batch normalization
3. Final output layer activated by Sigmoid
4. Use binary cross-entropy as loss function
5. Use Adam optimizer with the parameter
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