Official website of our paper: Applications of Deep Learning in Fundus Images: A Review. Newly-released datasets and recently-published papers will be updated regularly.
Actively maintained and comprehensive public glaucoma dataset catalog
AUTOMATED TYPE CLASSIFICATION OF GLAUCOMA DETECTION USING DEEP LEARNING
Glaucoma detection automation project. Trained a binary image classifier using CNNs and deployed as a streamlit web app. It takes eye (retinal scan) image as input and outputs whether the person is affected by glaucoma or not.
Standardized Multi-Channel Dataset for Glaucoma (SMDG-19) is a collection and standardization of 19 public full-fundus glaucoma images and associated metadata.
Glaucoma detection using deep learning(cnn)
Health Economics simulation
Repositório com a parte prática do meu Trabalho de Conclusão de Curso III, referente ao algoritmo da arquitetura CapsNet para a classificação de imagens de retina em glaucomatosas e normais.
Design and implement an algorithm in Java to test visual loss of Glaucoma patients. The algorithm is designed to imitate the real tests which are expensive and done on a special hardwares. The program can run on a normal computer screen reducing the cost and increasing the availability of the eye test in rural areas.
ACRIMA project
This GUI is specifically designed to facilitate the analysis and processing of medical fundus images, with a particular focus on the detection and classification of defects in the retinal nerve fiber layer, which is crucial for the diagnosis and monitoring of glaucoma.
IGFBPL1 Retinal Microglia Project | Dong Feng Chen Lab collaboration | Schepens Eye Research Institute, Mass General Hospital, Harvard Medical School
Convert hand written intraocular pressure graphs to csv.
Learning a thing or two about AI/ML in healthcare
Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma
Various Glaucoma related machine learning and computer vision projects done with Caprioli lab at Stein Eye.
"Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling" by Gregory Holste, Mingquan Lin, Ruiwen Zhou, Fei Wang, Lei Liu, Qi Yan, Sarah H Van Tassel, Kyle Kovacs, Emily Y Chew, Zhiyong Lu, Zhangyang Wang, & Yifan Peng
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