Project for segmentation of blood vessels, microaneurysm and hardexudates in fundus images.
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Updated
Aug 29, 2018 - Python
Project for segmentation of blood vessels, microaneurysm and hardexudates in fundus images.
Patho-GAN: interpretation + medical data augmentation. Code for paper work "Explainable Diabetic Retinopathy Detection and Retinal Image Generation"
A Django application developped for classification of a diabetes complication that affects eyes
[TMI'22] "AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation".
exudates detection using hybrid approach (Image Morphology & Machine Learning)
Deep learning applied to Kaggle's Diabetic retinopathy dataset.
Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models
Diabetic Retinopathy is a very common eye disease in people having diabetes. This disease can lead to blindness if not taken care of in early stages, This project is a part of the whole process of identifying Diabetic Retinopathy in its early stages. In this project, we'll extract basic features which can help us in identifying Diabetic Retinopa…
Joint Vessel Segmentation and Deformable Registration on Multi-Modal Retinal Images based on Style Transfer
Deep learning based retinal vessel segmentation for fluorescein angiography retinal images, IEEE Trans. Image Processing, 2020
ICML Workshop 18 - Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model
Deep learning based retinal vessel segmentation for wide-field fundus photography retinal images, IEEE Trans. Medical Imaging, 2020
Retinal image processing with python and opencv
Diabetic classification based on retinal images
ACCV'18 workshop - Synthesizing New Retinal Symptom Images by Multiple Generative Models
Code for the paper "OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing"
[ICIP'20] [Tensorflow] Improving robustness using Joint Supervised-Unsupervised Network for OCT images
The work presented herein describes the development and implementation of novel computer algorithms for quantitative analysis of SD-OCT images of live teleost eyes.
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