FDP on From Pixels to Predictions: Machine Learning in Medical Imaging 2023

The FDP is designed to empower educators and researchers with the knowledge and skills necessary to navigate the intersection of medical imaging and cutting-edge machine learning technologies. Through a comprehensive curriculum, participants will delve into the fundamentals of medical imaging, gain hands-on experience in building and training neural networks, and explore crucial ethical and regulatory considerations in deploying machine learning in healthcare. This FDP not only equips faculty members with the tools to stay abreast of industry trends and emerging technologies but also fosters a collaborative environment for networking and project development. At the end of the FDP participants will be well-prepared to integrate machine learning principles into their teaching and research, contributing to the advancement of medical imaging AI in both academic and clinical settings.

FDP

Day Topics to be Covered
1 Introduction to Medical Imaging and Machine Learning
• Overview of Machine Learning (ML) and its applications in medical imaging.
• Overview of medical imaging modalities (X-ray, CT, MRI, ultrasound)
• Types of machine learning algorithms (supervised, unsupervised, reinforcement learning)
• Challenges and opportunities in applying machine learning to medical imaging.
• Hands-on session: Introduction to Python and basic machine learning libraries
2 Image Preprocessing and Feature Extraction
• Image preprocessing techniques (noise reduction, segmentation, enhancement)
• Image Enhancement Techniques and modelling
• Feature extraction methods for medical images
• Handling imbalanced datasets in medical imaging.
• Hands-on session: Image preprocessing, enhancement and feature extraction using Python libraries
3 Deep Learning Architectures for Medical Image Analysis
• Introduction to deep learning for medical image analysis
• Convolutional neural networks (CNNs) for medical image classification and segmentation
• Transfer learning in medical imaging.
• Introduction to common machine/Deep learning libraries (e.g., TensorFlow, PyTorch).
• Fine-tuning pre-trained models for specific medical imaging tasks.
• Hands-on session: Building deep learning models for medical image analysis using Python libraries•
4 Model Evaluation and Deployment
• Evaluation metrics for medical image analysis (e.g., sensitivity, specificity)
• Techniques for model selection and hyperparameter optimization
• Cross-validation and hyperparameter tuning.
• Practical session on model evaluation and validation.
5 Ethics and Legal Considerations
• Addressing challenges and pitfalls in deploying machine/deep learning models in healthcare.
• Applications of machine learning in specific medical imaging tasks (e.g., tumor detection, organ segmentation).
• Emerging trends in machine learning for medical imaging.
• Ethical considerations in machine/deep learning for medical imaging
• Future directions of machine/deep learning in medical imaging

11.12.2023

• Faculty / Research Scholars – Rs. 400 (Including GST)
• Industry persons – Rs. 600 (Including GST)

Chennai Campus Vandalur - Kelambakkam Road Chennai - 600 127