四种不同的基于U-net的图像分割方法以及一个配套处理好的ISIC（表皮目标分割，最新官网下载的数据集）的数据、直接可跑

### pytorch Implementation of U-Net, R2U-Net, Attention U-Net, Attention R2U-Net **(This repository is no longer being updated)** **U-Net: Convolutional Networks for Biomedical Image Segmentation** https://arxiv.org/abs/1505.04597 **Recurrent Residual Convolutional Neural Network based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](https://challenge2018.isic-archive.com/task1/training/). 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.