-
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.
Federated Learning for Multi-Center Medical Image Diagnostics
Generalizable Medical Imaging AI, Powered by Federated Learning
About This Course
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.
Aim
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.
Workshop Objectives
-
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.
Workshop Structure
📅 Day 1 — The Setup & Data Preparation
- Focus: MRI Pre-processing & Federated Environment Initialization
- Key Topics:
- Understanding MRI modalities: T1, T2, FLAIR and their diagnostic relevance
- Image normalization, skull stripping, resizing, and augmentation
- Simulating secure, isolated federated “client” nodes
- Data partitioning strategies for multi-center simulation
- Hands-on (Google Colab):
- Notebook Title: MRI Pre-processing & Federated Client Setup
- Task: Pre-process multi-modal MRI datasets using MONAI transforms and configure multiple simulated client nodes with isolated local datasets
📅 Day 2 — The Core AI & Federated Implementation
- Focus: 3D U-Net with Federated Averaging (FedAvg)
- Key Topics:
- Architecture of 3D U-Net for volumetric tumor segmentation
- Federated Learning fundamentals and FedAvg algorithm workflow
- Local training vs global model aggregation
- Communication rounds and model convergence monitoring
- Hands-on (Google Colab):
- Notebook Title: Federated 3D U-Net Implementation with Flower
- Task: Build a 3D U-Net model in MONAI and implement Federated Averaging using Flower to collaboratively train across multiple clients without sharing raw MRI data
📅 Day 3 — Validation, Visualization & Publication Readiness
- Focus: Performance Benchmarking & Research Output Generation
- Key Topics:
- Comparative evaluation: Centralized vs Federated model performance
- Metrics: Dice score, IoU, Precision, Recall
- 3D tumor volume rendering for visualization
- Preparing figures for graphical abstracts and high-impact publications
- Hands-on (Google Colab):
- Notebook Title: Federated Model Evaluation & 3D Visualization
- Task: Extract validation metrics, generate performance comparison plots, and render 3D tumor segmentations suitable for research publication
Who Should Enrol?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Important Dates
Registration Ends
03/07/2026
IST 04:30 PM IST
Workshop Dates
03/07/2026 – 03/09/2026
IST 05:30PM IST
Workshop Outcomes
Fee Structure
Student
₹2499 | $75
Ph.D. Scholar / Researcher
₹3499 | $85
Academician / Faculty
₹4499 | $95
Industry Professional
₹6499 | $115
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
★★★★★
Cancer Drug Discovery: Creating Cancer Therapies
Mario Rigo • ★★★★★
Power BI and Advanced SQL Mastery Integration Workshop, CRISPR-Cas Genome Editing: Workflow, Tools and Techniques
Silvia Santopolo • ★★★★★
Artificial Intelligence for Cancer Drug Delivery
G Jyothi • ★★★★★
Artificial Intelligence for Cancer Drug Delivery
RAVIKANT SHEKHAR • View All Feedbacks →