| Attribute | Detail |
|---|---|
| Format | Recorded Lectures |
| Level | Beginner |
| Duration | 3 Days, 3 Hours per Day |
| Certification | e-Certification + e-Marksheet |
| Fee | Free |
| Tools | FSL ANTs FreeSurfer SPM Python scikit-learn PyTorch TensorFlow Nilearn BIDS |
About the AI-Powered Neuroimaging: Predicting Cognitive Decline Through MRI & fMRI Pattern Recognition 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.
Decode the brain, predict cognitive decline, and translate AI models to clinical practice.
Program Highlights
• Comprehensive coverage of Powered Neuroimaging from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Neuroimaging AI
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: FSL, ANTs, FreeSurfer, SPM
• Career-oriented training for academic and professional growth in Neuroimaging AI
Course Curriculum
Module 1: 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: 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: 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: 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: 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: 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
Tools, Techniques, or Platforms Covered
FSL ANTs FreeSurfer SPM Python scikit-learn PyTorch TensorFlow Nilearn BIDS
Real-World Applications
- Apply Powered Neuroimaging skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Neuroimaging AI competencies
- Solve industry-relevant problems using Powered Neuroimaging methodologies and tools
- Contribute to open-source projects and collaborative research in Neuroimaging AI
- Prepare for competitive examinations, interviews, and professional certifications in Neuroimaging AI
Who Should Attend & Prerequisites
- Industry‑recognised e‑Certification + e‑Marksheet from NSTC
- Hands‑on training with practical projects and industrial datasets (ADNI)
- Dedicated expert mentorship and doubt‑resolution throughout the program
Certification
