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

(This repository is forked from )

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

TransU-Net

Before use, we need first to download pretrain model.

• Get models in this link: R50-ViT-B_16, ViT-B_16, ViT-L_16... All the supported models:

• ViT-B_16
• ViT-B_32
• ViT-L_16
• R50+ViT-B_16

# This script will automatically download the pretrained models to the folder ./pretrain/imagenet21k
run_scripts/download_pretrained_models.sh

Evaluation

We just test the models with ISIC 2018 dataset task 1. 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 train dataset contains 2594 images, the validation dataset contains 100 images, the test dataset contains 1000 images.