# Image_Segmentation

**Repository Path**: m1a0up/Image_Segmentation

## Basic Information

- **Project Name**: Image_Segmentation
- **Description**: pytorch Implementation of U-Net, R2U-Net, Attention U-Net, Attention R2U-Net.
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 2
- **Created**: 2020-03-11
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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
![U-Net](/img/U-Net.png)


## R2U-Net
![R2U-Net](/img/R2U-Net.png)

## Attention U-Net
![AttU-Net](/img/AttU-Net.png)

## Attention R2U-Net
![AttR2U-Net](/img/AttR2U-Net.png)

## 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.

![evaluation](/img/Evaluation.png)