Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
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. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
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.
📋 Course Objectives
1. Translate AI Enablement theory into practical, reproducible analysis.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Day 1 – Setup & Data Preparation
Prepare multi‑modal MRI datasets using MONAI transforms • Configure isolated federated client nodes in Google Colab • Apply data partitioning strategies for realistic multi‑center simulation
Day 2 – Core AI & Federated Implementation
Build a 3D U‑Net for volumetric tumor segmentation with MONAI • Implement Federated Averaging (FedAvg) using the Flower framework • Orchestrate multi‑client training without sharing raw MRI data
Day 3 – Validation, Visualization & Publication Readiness
Benchmark federated vs. centralized models using Dice, IoU, precision, recall • Generate 3D tumor volume visualizations for research abstracts • Prepare figures and performance tables for high‑impact journal submission
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | MONAI |
| Covered Tool / Platform | Flower |
| Covered Tool / Platform | Google Colab |
Frequently Asked Questions
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