Awesome

AG-CNN

The model of "Attention-based CNN for Glaucoma Detection (AG-CNN)", has been published as "Attention Based Glaucoma Detection: A Large-scale Database with a CNN Model".

LAG-Database

The LAG database contains 11,760 fundus images corresponding to 4,878 suspicious and 6,882 negative glaucoma samples. All the samples are labelled with the diagnosis results (0 refers to negative glaucoma and 1 refers to suspicious glaucoma). 5,824 fundus images are further labelled with attention regions based on an alternative method for eye tracking, in which 2,392 are positive glaucoma and the rest 3,432 are negative glaucoma.

An example of capturing fixations of an ophthalmologist in glaucoma diagnosis.

Some samples from our LAG database.

Download

We have uploaded the first part of our LAG database at Dropbox under request. Please contact us for the password.

Note that the LAG database should ONLY be used for academic purposes and other usage is refused. Also, it is NOT allowed to re-upload the LAG database on the internet.

Licence

Our work is conducted according to the tenets of the Helsinki Declaration. As the retrospective nature and fully anonymized usage of colour retinal fundus images, we are exempted by the medical ethics committee from informing the patients.

Citation

The conference version of our work has been published in CVPR2019, one can cite with the Bibtex code:

@InProceedings{Li_2019_CVPR,
author = {Li, Liu and Xu, Mai and Wang, Xiaofei and Jiang, Lai and Liu, Hanruo},
title = {Attention Based Glaucoma Detection: A Large-Scale Database and CNN Model},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

Contact

If any questions, please contact ll1320@ic.ac.uk