Bayesian Deep Learning Benchmarks

Papers and Public Datasets for Diabetic Retinopathy Detection

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

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

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

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

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

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

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)

FFA Synthesis from CFP （ACM MM 2024 Workshop）

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…