Programme Parameters
Foundation
3 Days (4.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Comprehensive hands‑on curriculum covering data acquisition, preprocessing pipelines, classical machine‑learning, and state‑of‑the‑art deep‑learning methods for structural and functional neuroimaging analysis. Across 4 Weeks, you will build practical fluency in data acquisition, preprocessing pipelines, and functional neuroimaging analysis, then consolidate everything in a capstone project. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Comprehensive hands‑on curriculum covering data acquisition, preprocessing pipelines, classical machine‑learning, and state‑of‑the‑art deep‑learning methods for structural and functional neuroimaging analysis.
📋 Course Objectives
1. Develop hands-on skill in data acquisition.
2. Master the fundamentals of preprocessing pipelines.
3. Get comfortable working with functional neuroimaging analysis.
4. Put biotechnology techniques to work on real datasets and case studies.
5. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
• Data and computational scientists moving into data acquisition
🚀 Key Learning Outcomes
• Confidence to implement data acquisition in real projects.
• Confidence to reason about preprocessing pipelines in real projects.
• Confidence to apply functional neuroimaging analysis in real projects.
• Tangible, reproducible biotechnology 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
Module 1 – Neuroimaging Foundations & Pre‑processing (Day 1)
Understand T1 relaxation, VBM, and tissue contrast for gray‑ and white‑matter mapping • Apply BIDS standards, slice‑timing, distortion, and motion correction for robust MRI data • Execute brain extraction, bias‑field correction, and registration to MNI space using FSL, ANTs, and HD‑BET
Module 2 – Classical Machine Learning for MRI/fMRI (Day 2)
Construct feature matrices from GM volumes, cortical thickness, and functional connectivity edges • Perform feature selection, nested cross‑validation, and model evaluation (AUC‑ROC, balanced accuracy) • Implement SVM, Random Forest, and LASSO pipelines on structural and diffusion metrics
Module 3 – Deep Learning Architectures (Day 3)
Build 3D‑CNN, ResNet‑3D, and DenseNet‑3D models for volumetric T1w classification • Explore Vision Transformers (ViT) and Graph Neural Networks for multimodal fusion • Apply U‑Net for hippocampal segmentation and interpret models with GradCAM, Integrated Gradients, LIME, SHAP
Module 4 – Clinical Translation & Validation
Learn TRIPOD‑AI reporting, FDA SaMD considerations, and API inference skeletons • Integrate multi‑site harmonization (ComBat) and external validation on ADNI datasets • Design deployment pipelines for real‑time cognitive‑decline risk scoring
Module 5 – Explainability & Ethical AI
Implement SHAP DeepExplainer and attention‑map visualisation for model transparency • Assess bias, fairness, and privacy in neuroimaging AI pipelines • Document reproducible research workflows using Jupyter and Git
Module 6 – Capstone Project
Apply end‑to‑end pipeline on a real ADNI subset to predict MCI‑to‑AD conversion • Generate a clinical report with model performance, interpretability visualisations, and deployment script • Present findings to peer mentors and receive feedback for improvement
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | FSL |
| Covered Tool / Platform | ANTs |
| Covered Tool / Platform | FreeSurfer |
| Covered Tool / Platform | SPM |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | scikit-learn |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Nilearn |
| Covered Tool / Platform | BIDS |
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