Federated Learning for Multi-Center Medical Image Diagnostics
Generalizable Medical Imaging AI, Powered by Federated Learning
Skills you will gain:
About Program:
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.
Program Objectives:
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To introduce the fundamental principles and architecture of Federated Learning (FL) in healthcare.
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To understand challenges in multi-center medical imaging AI, including data heterogeneity and bias.
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To explore privacy-preserving techniques such as secure aggregation and differential privacy.
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To demonstrate federated model training workflows using real-world MRI, CT, and X-ray use cases.
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To develop strategies for building scalable, regulation-compliant diagnostic AI systems for clinical deployment.
What you will learn?
📅 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
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- 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.
Career Supporting Skills
Program Outcomes
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Explain the core concepts and workflow of Federated Learning in healthcare AI.
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Design a basic multi-center federated training architecture for medical imaging.
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Identify and address challenges such as data heterogeneity, bias, and communication efficiency.
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Apply privacy-preserving techniques including secure aggregation and differential privacy.
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Evaluate the performance and generalizability of federated models for MRI, CT, and X-ray diagnostics.
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Outline strategies for deploying compliance-ready, scalable AI systems in clinical environments.