AI-Powered Digital Pathology
Transforming Diagnostics with Deep Science AI: From Pixels to Prognosis
Skills you will gain:
The integration of artificial intelligence into pathology represents a transformative shift in medical diagnostics. This program offers a deep science learning experience, focusing on AI model development, training, interpretability, and deployment within clinical workflows. Participants will be exposed to cutting-edge practices in transfer learning, ensemble modeling, and explainable AI (XAI) for pathology, ensuring a robust understanding of how technology augments traditional diagnostic tools.
Over four weeks, learners will explore regulatory compliance, ethical challenges, and medico-legal issues, balanced with practical case studies in oncology and dermatopathology. The course also covers future trends like federated learning and multi-modal imaging. This program is designed for those passionate about the future of medical diagnostics and innovation in healthcare.
Aim:
To equip participants with comprehensive knowledge and practical expertise in building, validating, and deploying AI models for digital pathology, integrating ethical and regulatory frameworks, and exploring real-world clinical applications through hands-on learning and expert insights.
Program Objectives:
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Understand the principles of AI model development in digital pathology
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Gain hands-on experience in CNN training, optimization, and transfer learning
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Analyze regulatory and ethical implications in AI diagnostics
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Examine real-world case studies and clinical deployment insights
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Explore future technologies and career pathways in AI-driven healthcare
What you will learn?
Week 1: Developing AI Models for Pathology
- Data preparation, model selection, validation
- CNN training and optimization
- Transfer learning, ensemble models, XAI
- Clinical workflow integration
- Deployment challenges
Week 2: Regulatory and Ethical Considerations
- Regulatory compliance and approvals
- Ethical issues in AI diagnostics
- Model interpretability and transparency
- Bias and fairness management
- Risk and medico-legal aspects
Week 3: Case Studies and Applications
- AI applications in oncology
- AI in dermatopathology
- Digital diagnostics integration
- Industry expert panel discussions
- Clinical deployment insights
Week 4: Future Trends and Closure
- Multi-modal imaging and federated learning
- Future of AI in medical imaging
- Career and research opportunities
- Networking and program closure
Intended For :
- Undergraduate degree in Life Sciences, Biomedical Engineering, Pathology, Computer Science, or related fields.
- Medical professionals, data scientists, and AI/ML engineers exploring healthcare.
- Individuals with a keen interest in deep science innovation and digital health technologies.
Career Supporting Skills