Program Structure

Module 1: Introduction to Digital Pathology

• What is digital pathology? An overview of digital imaging and whole slide scanning technology.
• Benefits of digital pathology: remote access, faster diagnoses, improved data storage and sharing.
• Introduction to digital pathology slides: slide preparation, scanning techniques, and file formats (e.g., TIFF, SVS).
• Comparison between traditional microscopy and digital pathology systems.

Module 2: Image Acquisition and Processing

• Basics of image acquisition: high-resolution scanning, multi-spectral imaging, and stain-based imaging techniques.
• Preprocessing techniques: noise reduction, normalization, and contrast enhancement.
• Image segmentation: techniques for identifying regions of interest, tissue structures, and boundaries of cells and lesions.
• Feature extraction: quantifying morphology, color, texture, and spatial relationships in pathology slides.

Module 3: Artificial Intelligence in Digital Pathology

• Introduction to AI and machine learning: supervised vs unsupervised learning, neural networks, and deep learning fundamentals.
• Deep learning in pathology: convolutional neural networks (CNNs) for image recognition and classification.
• AI model training: dataset preparation, data augmentation, model validation, and cross-validation techniques.
• Challenges in AI for digital pathology: data quality, dataset bias, interpretability, and regulatory concerns.

Module 4: Applications of AI in Pathology

• Cancer detection and classification: AI-driven tumor detection, staging, and grading.
• Biomarker identification: using AI to identify molecular markers from histopathology slides.
• Quantifying immune responses: AI models for immune cell identification and assessment of immune microenvironments.
• Prognostic prediction: using AI models to predict patient outcomes and survival rates based on tissue analysis.

Module 5: Data Integration in Digital Pathology

• Integrating clinical and genomic data with digital pathology images for precision medicine.
• Leveraging multi-modal data: combining pathology, imaging, and molecular profiling for comprehensive diagnostics.
• Clinical decision support systems: how AI-based pathology systems assist clinicians in diagnosis and treatment planning.
• Data interoperability: challenges in data sharing, standardization, and integration across healthcare platforms.

Module 6: Regulatory and Ethical Considerations in AI Pathology

• Regulatory landscape: FDA, CE marking, and other regulatory standards for AI applications in healthcare.
• Data privacy and security concerns: HIPAA, GDPR, and ethical considerations in handling patient data.
• Bias in AI models: understanding and mitigating biases in AI algorithms used in medical applications.
• Ethical decision-making in AI pathology: transparency, accountability, and patient consent.

Module 7: Building AI Models for Pathology Image Analysis

• Steps for building AI models: dataset collection, pre-processing, training, and evaluation.
• Image classification tasks: classifying tissues, identifying tumor types, and staging.
• Object detection tasks: locating lesions, cells, or other key structures within the image.
• Model evaluation metrics: accuracy, sensitivity, specificity, ROC curves, and precision-recall.

Module 8: Future Trends in Digital Pathology and AI

• The future of AI in pathology: increasing accuracy, expanding applications, and integrating multi-omics data.
• Real-time diagnostics: AI-based tools for point-of-care testing and decision support.
• AI-powered automation: increasing efficiency in laboratory workflows, data analysis, and reporting.
• The role of augmented reality (AR) and virtual reality (VR) in enhancing AI-driven pathology workflows.

Final Project

• Create a Digital Pathology AI System Blueprint for a specific clinical or research application.
• Include: system architecture, data collection plan, AI model design, application strategy, and integration with clinical workflows.
• Example projects: AI model for cancer diagnosis in breast tissue slides, automated detection of liver fibrosis, biomarker identification system for lung cancer, or prognostic prediction model for melanoma using histopathology images.