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EyeNet2

★ 17 Open GitHub ↗

ICML Workshop 18 - Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model

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# EyeNet2, U-Net Segementation on Drive, ACCV 2018
If you think this repo helps your research, please consider ref this paper (ACCV Workshop 2018, oral.) Thanks!
A U-Net Segmentation is trained on the classical [Drive](https://drive.grand-challenge.org/) (Utrecht University) dataset. (our model was released in 2017)

Georgia Tech, KAUST, U Waterloo Kyoto U

[Yang, C-H. Huck, Fangyu Liu et al. "Auto-classification of retinal diseases in the limit of sparse data using a two-streams machine learning model." Asian Conference on Computer Vision. Springer, Cham, 2018.](https://arxiv.org/pdf/1808.05754.pdf)

```
@inproceedings{yang2018auto,
  title={Auto-classification of retinal diseases in the limit of sparse data using a two-streams machine learning model},
  author={Yang, C-H Huck and Liu, Fangyu and Huang, Jia-Hong and Tian, Meng and Lin, MD I-Hung and Liu, Yi Chieh and Morikawa, Hiromasa and Yang, Hao-Hsiang and Tegn{\`e}r, Jesper},
  booktitle={Asian Conference on Computer Vision},
  pages={323--338},
  year={2018},
  organization={Springer}
}
```
Supplymentary 2019

# Run

```shell
python run_training.py
```

## Demo: U-Net Segmentation of Retinal Vessel

(a) Test Image (b) Ground Truth (c) Automatic Segementation after U-Net Image Model

## PR-Curve of U-Net for Retina

## ROC of U-Net for Retina

EyeNet2

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