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Awesome Open Source

pytorch Implementation of U-Net, R2U-Net, Attention U-Net, 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)





Attention U-Net


Attention R2U-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.


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Python (838,168
Dataset (33,106
Pytorch (21,672
Validation (20,380
Segmentation (8,330
Attention (3,889