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 🚂
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.
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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.
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Oct 10, 2025
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