[MedIA'24] FLAIR: A Foundation LAnguage-Image model of the Retina for fundus image understanding.
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Updated
May 15, 2024 - Python
[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 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
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
RET-CLIP: A Retinal Image Foundation Model Pre-trained with Clinical Diagnostic Reports
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
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
Preprocessing images
This project is to build automatic image quality assessment model for fundus 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.
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