Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.
U-Net: Convolutional Networks for Biomedical Image Segmentation
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
Attention U-Net: Learning Where to Look for the Pancreas
Attention R2U-Net : Just integration of two recent advanced works (R2U-Net + Attention U-Net)
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.