Bayesian Deep Learning Benchmarks

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

Papers and Public Datasets for Diabetic Retinopathy Detection

A Django application developped for classification of a diabetes complication that affects eyes

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.

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

Dopamine: Differentially Private Federated Learning on Medical Data (AAAI - PPAI)

This is the official implementation of the paper Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images.

🥉 (Bronze medal - 163rd place - Top 6%) Repository for the "APTOS 2019 Blindness Detection" Kaggle competition.

A Deep Convolutional Neural Network for Diabetic Retinopathy classification

Diabetic Retinopathy Feature Extraction and Binary Diagnosis

[MICCAI'24 Early Accept] Generalizing to Unseen Domains in Diabetic Retinopathy with Disentangled Representations

This repo is the official implementation of TMI2024 paper "Prompt-driven Latent Domain Generalization for Medical Image Classification".

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

Retinal Lesions (Microaneurysms, Hard Exudates, Soft Exudates, Hemorrhages) Segmentation using Deep Learning Pipeline and Image Processing & Machine Learning Pipeline

Diabetic classification based on retinal images

The funds image quality label is provided by iMed (homepage: http://imed.nimte.ac.cn/ ; http://imed.nimte.ac.cn/aboutus.html)

The project addresses automatic detection of microaneurysms (MA) which are first detectable changes in Diabetic Retinopathy (DR). Green channel, being the most contrasted channel, of the color fundus images are considered. The algorithm includes pre-processing, MA candidates detection, features extraction, classification and comparison with grou…

[TMI 2024] Code for "Concept-based Lesion Aware Transformer for Interpretable Retinal Disease Diagnosis"

This is a Categorical Detection and Prediction Task based on subset of a Kaggle dataset from Eye Images (Aravind Eye hospital) - APTOS 2019 Challenge. The goal is to predict the Blindness Stage (0-4) class from the Eye retina Image using Deep Learning Models (transfer learning via resnet50). This Automated System would speed up Blindness detecti…