pytorch Implementation of U-Net, R2U-Net, Attention U-Net, Attention R2U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://arxiv.org/abs/1505.04597
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
https://arxiv.org/abs/1802.06955
Attention U-Net: Learning Where to Look for the Pancreas
https://arxiv.org/abs/1804.03999
Attention R2U-Net : Just integration of two recent advanced works (R2U-Net + Attention U-Net)
U-Net
R2U-Net
Attention U-Net
Attention R2U-Net
Evaluation
we just test the models with ISIC 2018 dataset. The dataset was split into three subsets, training set, validation set, and test set, which the proportion is 70%, 10% and 20% of the whole dataset, respectively. The entire dataset contains 2594 images where 1815 images were used for training, 259 for validation and 520 for testing models.
