Integrating Machine Learning and Mathematical Computing for Advanced Applications in Medical Imaging

• To introduce participants to the fundamentals of machine learning and mathematical computing for medical imaging.
• To train participants in implementing ML algorithms and mathematical techniques for medical image analysis.
• To explore real-world applications of AI in medical diagnostics and treatment planning.
• To address ethical considerations and best practices in AI-driven medical imaging.
• To prepare participants for research and professional roles at the intersection of AI, mathematics, and healthcare.

Day 1: Foundations of Mathematical Computing in Medical Imaging

• Introduction to Mathematical Computing
• Overview of mathematical concepts: Linear Algebra and Calculus in medical imaging
• Applications of Fourier Transforms in image analysis
• Interactive Q&A and discussion

Day 2: Machine Learning Fundamentals for Medical Imaging

• Introduction to Machine Learning in Medical Imaging
• Key algorithms: SVM, k-NN, and Decision Trees
• Case study: Image classification and segmentation
• Q&A and hands-on insights

Day 3: Integrating Mathematical Models with Machine Learning

• Combining mathematical computing with ML algorithms
• Real-world applications: Predictive analysis and diagnostics
• Practical challenges and evaluation techniques
• Workshop conclusion and certificate distribution

• Biomedical engineers, radiologists, and healthcare professionals
• Data scientists and ML engineers working in healthcare
• Academics and researchers in AI, mathematics, and medical imaging
• Students pursuing careers in biomedical sciences and technology