Generalizable Medical Imaging AI, Powered by Federated Learning
Federated Learning for Multi-Center Medical Image Diagnostics explores how hospitals and research centers can collaboratively train medical imaging AI models without sharing patient data. Participants will learn the core federated learning workflow, multi-center training setup, model aggregation, and privacy/security essentials (e.g., secure aggregation, differential privacy). The workshop highlights real diagnostic imaging use cases (CT, MRI, X-ray) and focuses on building robust, generalizable, compliance-ready AI models for real-world deployment.
To equip participants with the knowledge and practical understanding of federated learning for building privacy-preserving, multi-center medical imaging AI models, enabling collaborative training across hospitals without sharing sensitive patient data while improving model generalization, reliability, and deployment readiness.
To introduce the fundamental principles and architecture of Federated Learning (FL) in healthcare.
To understand challenges in multi-center medical imaging AI, including data heterogeneity and bias.
To explore privacy-preserving techniques such as secure aggregation and differential privacy.
To demonstrate federated model training workflows using real-world MRI, CT, and X-ray use cases.
To develop strategies for building scalable, regulation-compliant diagnostic AI systems for clinical deployment.
📅 Day 1 — The Setup & Data Preparation
📅 Day 2 — The Core AI & Federated Implementation
📅 Day 3 — Validation, Visualization & Publication Readiness
March 7, 2026
IST 04:30 PM IST
March 7, 2026 – March 9, 2026
IST 05:30PM IST
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Explain the core concepts and workflow of Federated Learning in healthcare AI.
Design a basic multi-center federated training architecture for medical imaging.
Identify and address challenges such as data heterogeneity, bias, and communication efficiency.
Apply privacy-preserving techniques including secure aggregation and differential privacy.
Evaluate the performance and generalizability of federated models for MRI, CT, and X-ray diagnostics.
Outline strategies for deploying compliance-ready, scalable AI systems in clinical environments.
₹2,499
$50
The live sessions of this workshop are over, so the live fees no longer apply. The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.
Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).
Watch the recording in your dashboard and send your questions to the mentor through the support desk.
Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.
Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
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Registration open
Live, mentor-led and certified. Dates and times in IST.
