📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Explainable AI (XAI) in Digital Pathology, from foundations to a certified capstone project.
Outline
Define the landscape of AI in Digital Pathology and its challenges. • Understand the fundamental concepts of Explainable AI (XAI). • Explore the ethical and regulatory importance of explainability in healthcare.
Outline
Review essential image processing techniques for whole slide images (WSIs). • Identify key features and patterns in pathological specimens. • Prepare datasets for AI model training and evaluation.
Outline
Examine inherently interpretable models like decision trees and linear models. • Implement and evaluate simple diagnostic models. • Compare their interpretability advantages and limitations.
Outline
Apply LIME (Local Interpretable Model-agnostic Explanations) to image data. • Utilize SHAP (SHapley Additive exPlanations) for feature importance. • Generate heatmaps with Grad-CAM and its variants for visual explanations.
Outline
Implement counterfactual explanations to understand 'what if' scenarios. • Discuss adversarial attacks and defenses in medical image analysis. • Evaluate the robustness of XAI models in digital pathology.
Outline
Develop strategies for integrating XAI into clinical workflows. • Address challenges in validating and deploying explanation systems. • Explore emerging trends and research directions in XAI for healthcare.
Earn government-registered certification in Explainable AI (XAI) in Digital Pathology
e-Certificate and e-Marksheet issued on successful completion.
