update README.md

README.md

@@ -0,0 +1,49 @@
+### CA-Net: Comprehensive attention Comvolutional Neural Networks for Explainable Medical Image Segmentation
+This repository provides the code for "CA-Net: Comprehensive attention Comvolutional Neural Networks for Explainable Medical Image Segmentation". The paper can be found at: 
+
+![mg_net](./pictures/canet.png)
+Fig. 1. Structure of CA-Net.
+
+![uncertainty](./pictures/uncertainty.png)
+Fig. 2. Skin lesion segmentation.
+
+![refinement](./pictures/refinement.png)
+
+Fig. 3. Placenta and fetal brain segmentation.
+
+### Requirements
+Some important required packages include:
+* [Pytorch][torch_link] version >=0.4.1.
+
+Follow official guidance to install [Pytorch][torch_link]. Install the other required packages by:
+```
+pip install -r requirements.txt
+```
+
+[torch_link]:https://pytorch.org/
+
+### How to use
+After installing the required packages, add the path of `UGIR` to the PYTHONPATH environment variable. 
+### Demo of MG-Net
+1. Run the following commands to use MG-Net for simultanuous segmentation and uncertainty estimation. 
+```
+cd uncertainty_demo
+python ../util/custom_net_run.py test config/mgnet.cfg
+```
+2. The results will be saved to `uncertainty_demo/result`. To get a visualization of the uncertainty estimation in an example slice, run: 
+```
+python show_uncertanty.py
+```
+
+### Demo of I-DRLSE
+To see a demo of I-DRLSE, run the following commands:
+```
+cd util/level_set
+python demo/demo_idrlse.py 
+```
+The result should look like the following.
+![i-drlse](./pictures/i-drlse.png)
+
+### Copyright and License
+Copyright (c) 2020, University of Electronic Science and Technology of China.
+All rights reserved. This code is made available as open-source software under the BSD-3-Clause License.