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
Real-World Applications Apply Integrating Machine Learning and Mathematical Computing for Advanced Applications in Medical Imaging skills directly to academic research, thesis work, and publications. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Real-World Applications
Apply Integrating Machine Learning and Mathematical Computing for Advanced Applications in Medical Imaging skills directly to academic research, thesis work, and publications
📋 Course Objectives
1. Translate AI Enablement theory into practical, reproducible analysis.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 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
The Mathematics of the Image
• Acquisition physics for CT, MRI and ultrasound and the artefacts each produces
• DICOM structure, spacing, orientation and the metadata errors that break pipelines
• Reconstruction as an inverse problem, with filtered back projection as the reference
Classical Image Operations
• Intensity normalisation, bias field correction and resampling
• Filtering, denoising and the total variation and wavelet approaches
• Registration: rigid, affine and deformable, with the similarity metrics used
Delineating Structure
• Thresholding, region growing and level sets before the deep learning era
• U-Net and its variants, and why they still dominate medical segmentation
• Dice, Hausdorff distance and inter-observer variability as the accuracy ceiling
Models on Imaging Data
• Patch-based and 3D training under severe memory constraints
• Small datasets, augmentation and self-supervised pretraining
• Radiomics feature extraction and the reproducibility problem it has
Validation and Clinical Reality
• Patient-level splits, because slice-level splitting leaks systematically
• Scanner and protocol shift between sites as the usual cause of failure
• Reader studies, regulatory expectations and reporting under CLAIM
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | XGBoost |
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