fundus-image-analysis

[MedIA'25] 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 is a project leveraging state-of-the-art deep learning architectures to analyze and classify ocular diseases based on medical imaging data. This repository implements advanced machine learning techniques and modern neural network architectures to push the boundaries of intelligent recognition

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

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.

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

[TMI 2025] Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and Interpretation

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.

Deep ConvNets based eye cancer detection

Deep learning pipeline for classification of Cataract, Diabetic Retinopathy, Glaucoma and Normal using fundus images

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

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".

Information about training model and GradCam

Diabetic Retinopathy using Patch Networks.

Diabethic Retinopathy and Macular Degeration Detection with CNNs

📊 Analyze learning curves to diagnose model performance and enhance predictions using real-world housing data in this hands-on exploration of machine learning.

[ICCV'21] [Tensorflow] Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers