[MedIA'24] FLAIR: A Foundation LAnguage-Image model of the Retina for fundus image understanding.

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

ODIR-2019. Ocular Disease Intelligent Recognition Through Deep Learning Architectures

Optic Disc and Optic Cup Segmentation using 57 layered deep convolutional neural network

An adaptive threshold based algorithm for optic disc and cup segmentation in fundus images

EasyTorch is a research-oriented pytorch prototyping framework with a straightforward learning curve. It is highly robust and contains almost everything needed to perform any state-of-the-art experiments.

RET-CLIP: A Retinal Image Foundation Model Pre-trained with Clinical Diagnostic Reports

Deep learning based retinal vessel segmentation for wide-field fundus photography retinal images, IEEE Trans. Medical Imaging, 2020

Classification of Fundus Images into 5 stages of Diabetic Retinopathy, and segmentation of blood vessels in fundus images

Deep ConvNets based eye cancer detection

This research enhances early disease diagnosis by analyzing retinal blood vessels in fundus images using deep learning. It employs eight pre-trained CNN models and Explainable AI techniques.

Auto Retinal Disease Detection (ARDD) is the winning webapp of the 2020 Congressional App Challenge for Virginia's 10th District.

Information about training model and GradCam

Diabetic Retinopathy using Patch Networks.

Diabethic Retinopathy and Macular Degeration Detection with CNNs

This project is to build automatic image quality assessment model for fundus images

Preprocessing images

Real-time retinal tracking for ophthalmic applications. Optimized for low quality funduscopy data and for high detection and low error rates.

Our custom AI Pipeline on Fundus disease for 2019 Konyang-hackathon.