Hazrat-Ali9/Federated-Self-Supervised-Multimodal-Retina-Screening-under-Label-Noise-and-Differential-Privacy

🤡 Federated 🤖 Supervised 🍔 Multimodal 🍏 Retina Screening 🍎 Label Noise 🫑 Differential ✈ advanced AI 🚁 driven medical 🚀 imaging ⛱ research 🚟 that combines 🛬 the power of 🚞 Federated 🛸 Learning 🚅 Supervised 🚢 Learning ⛴ Differential 🏡 to enable 🛖 secure 🏭 collaborative 🏟 privacy 🏰 preserving 🏪 retinal 🕌 disease 🏨 detection 🚂

24
/ 100
Experimental

This project helps medical professionals, especially ophthalmologists and researchers, securely detect retinal diseases like diabetic retinopathy or glaucoma. It takes retinal images (fundus and OCT scans) from multiple clinics without centralizing them, then identifies potential diseases and provides visual explanations of its findings. This tool is designed for healthcare institutions that need to collaborate on AI model training while strictly maintaining patient data privacy and confidentiality.

No commits in the last 6 months.

Use this if you are a medical researcher or institution needing to collaboratively develop powerful AI models for retinal disease detection using diverse image data, without ever sharing raw patient data or compromising privacy.

Not ideal if you are a single clinic with a small, well-labeled dataset and no need for collaborative model training or advanced privacy protections.

ophthalmology medical imaging retinal disease screening healthcare AI data privacy
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 7 / 25
Maturity 15 / 25
Community 0 / 25

How are scores calculated?

Stars

36

Forks

Language

License

MIT

Last pushed

Oct 10, 2025

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Hazrat-Ali9/Federated-Self-Supervised-Multimodal-Retina-Screening-under-Label-Noise-and-Differential-Privacy"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.