Skip to content
/ AGNet Public
forked from HzFu/AGNet

The code of "Attention Guided Network for Retinal Image Segmentation" in MICCAI 2019

Notifications You must be signed in to change notification settings

kr-viku/AGNet

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

8 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Attention Guided Network for Retinal Image Segmentation (AG-Net)

The code of "Attention Guided Network for Retinal Image Segmentation" in MICCAI 2019.

  • The code is based on: Python 2.7 + pytorch 0.4.0.
  • You can run <AG_Net_path>/code/test.py for testing any new image directly.
  • You can run <AG_Net_path>/code/main.py for training a new model.

Quick usage on your data:

  • Put your desired file in "<AG_Net_path>/data/<your_file_name>".
  • Put the images in "<AG_Net_path>/data/<your_file_name>/images".
  • Put the labels in "<AG_Net_path>/data/<your_file_name>/label".
  • Divide data into training and test data, and store the image name in the "train_dict.pkl" file. (We provide a 'train_dict.pkl' sample for DRIVE dataset)
  • The "train_dict.pkl" should contains two dictionary: 'train_list' and 'test_list'.

Train your model with:

python main.py --data_path '../data/your_file_name'

###Reference

  1. S. Zhang, H. Fu, Y. Yan, Y. Zhang, Q. Wu, M. Yang, M. Tan, Y. Xu, "Attention Guided Network for Retinal Image Segmentation," in MICCAI, 2019. [PDF]
  2. H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation,” IEEE Trans. Med. Imaging, vol. 37, no. 7, pp. 1597–1605, 2018.

About

The code of "Attention Guided Network for Retinal Image Segmentation" in MICCAI 2019

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%