About
“Digital Pathology and AI-Driven Image Analysis” is a groundbreaking one-month course that merges the fields of pathology, digital imaging, and artificial intelligence to transform the way pathological analyses are performed. The course begins with an overview of digital pathology, including the basics of image acquisition and the transition from traditional microscopic methods to advanced digital systems. The second part of the course delves into the integration of AI and machine learning techniques, teaching students how to develop, train, and implement models that can automatically detect, classify, and predict pathological patterns and outcomes from digital slides.
Aim
This program is designed to equip participants with the skills to revolutionize pathology through digital technologies and artificial intelligence. Students will learn to harness powerful AI tools to enhance the accuracy and efficiency of pathology analysis, translating complex digital images into actionable medical insights.
Program Objectives
- Understand the fundamentals of digital pathology and image acquisition.
- Learn to use image processing tools and techniques to analyze pathology slides.
- Develop and apply AI models for automatic detection and classification of diseases.
- Evaluate the effectiveness of AI-driven image analysis in clinical practice.
- Promote the adoption of digital and AI technologies in pathology for improved healthcare outcomes.
Program Structure
Week 1:
Introduction to Digital Pathology
Understanding the shift from traditional to digital pathology
Overview of digital imaging systems and slide scanners
Fundamentals of digital pathology workflows
Imaging Techniques in Pathology
Basics of histopathological imaging
Key imaging modalities (brightfield, fluorescence, and multispectral imaging)
Quality control and standardization in digital imaging
Week 2:
Image Processing Fundamentals
Introduction to image processing techniques
Image pre-processing: Noise reduction, contrast enhancement, and normalization
Tissue segmentation and feature extraction
AI in Image Analysis
Introduction to machine learning and deep learning for image analysis
Practical session: Using AI tools for tissue classification
Case studies: Automated diagnosis and quantification in pathology
Week 3:
Developing AI Models for Pathology
Steps in building AI models: Data preparation, model selection, and validation
Training and optimizing convolutional neural networks (CNNs) for pathology
Exploring advanced AI techniques: Transfer learning and ensemble models
Integrating AI into Clinical Workflows
Challenges in clinical deployment of AI models
Regulatory and ethical considerations in AI-based diagnostics
Week 4:
Case Studies and Real-world Applications
AI applications in oncology, dermatopathology, and other specialties
Industry insights: Panel discussion with AI and pathology experts
Future Directions in AI for Pathology
Emerging trends in AI and pathology integration
Networking session and program closure
Participant’s Eligibility
- Undergraduate degree in Pathology, Biomedical Engineering, Computer Science, or related fields.
- Professionals in healthcare, biotechnology, or medical research sectors.
- Individuals interested in applying digital and AI technologies to enhance diagnostic and research capabilities in pathology.
Program Outcomes
- Mastery of digital slide preparation and analysis.
- Ability to apply AI and machine learning models to pathology images.
- Skills in automating and enhancing diagnostic processes.
- Knowledge of current and emerging technologies in digital and computational pathology.
- Capability to lead digital transformation in pathology departments.
Program Deliverables
- Access to e-LMS
- Real-Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet