GF-Care (Glaucoma Fundus – Cup-to-disc ratio Assessment for Retinal Evaluation) A deep learning-based system for automatic segmentation of the optic disc and cup from fundus images, enabling cup-to-disc ratio (CDR) calculation to support early glaucoma detection.

Helper framework for Medical Image Analysis

Diabetic Retinopathy Detection using Deep Learning.

Api to host Retfound

A Python package for computing the recall and precision scores specifically on thin vessels in retinal images and generating weight masks for BCE Loss to enhance models perfomance on segmenting these fine structures, as detailed in the paper "Vessel-Width-Based Metrics and Weight Masks for Retinal Blood Vessel Segmentation".

🩺 Enhance glaucoma detection with GF-Care, a deep learning tool for automatic optic disc and cup segmentation from fundus images for accurate CDR calculation.

[ACL 2024] Evaluation of the Fundus News Scraper

An implementation of "Fundus Image Transformation Revisited: Towards Determining More Accurate Registrations" in MATLAB.

Standardized Multi-Channel Dataset for Glaucoma (SMDG-19) is a collection and standardization of 19 public full-fundus glaucoma images and associated metadata.

Official repository of the paper "RRWNet: Recursive Refinement Network for Effective Retinal Artery/Vein Segmentation and Classification", published in Expert Systems with Applications (Dec 2024).

Optic disk is detected using image processing

PyTorch implementation for our paper on TMI2022: Retinal Vessel Segmentation with Skeletal Prior and Contrastive Loss

The Hamilton Eye Institute Macular Edema Dataset (HEI-MED) (formerly DMED) is a collection of 169 fundus images to train and test image processing algorithms for the detection of exudates and diabetic macular edema. The images have been collected as part of a telemedicine network for the diagnosis of diabetic retinopathy

Actively maintained and comprehensive public glaucoma dataset catalog

A python package to read, analyse and visualize OCT and fundus data from various sources.

[MICCAI'21] [Tensorflow] Retinal Vessel Segmentation using a Novel Multi-scale Generative Adversarial Network

Patho-GAN: interpretation + medical data augmentation. Code for paper work "Explainable Diabetic Retinopathy Detection and Retinal Image Generation"

Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development

exudates detection using hybrid approach (Image Morphology & Machine Learning)

Project for segmentation of blood vessels, microaneurysm and hardexudates in fundus images.