AI Model Development for Digital Pathology

Digital pathology has revolutionized diagnostic workflows by converting histological slides into high-resolution digital images. The integration of AI allows pathologists to analyze large datasets with increased speed, precision, and reproducibility. This workshop covers the fundamentals of AI in pathology, including image preprocessing, feature extraction, and model training for disease detection and classification. Participants will learn to apply AI algorithms to real-world pathology datasets, develop predictive models, and validate their performance. Emphasis will be placed on practical applications in oncology, hematology, and other clinical specialties, providing insights into improving diagnostic accuracy and enabling personalized patient care.

This workshop aims to provide participants with hands-on experience in developing AI models for digital pathology. It focuses on leveraging machine learning and deep learning techniques to analyze histopathological images, identify disease patterns, and assist in accurate diagnostics, ultimately enhancing clinical decision-making

Day 1 – Convolutional Neural Networks

• CNNs and their role in pathology
• CNN architecture (convolution, pooling, activation)
• Pathology-specific challenges: stain variation, magnification levels
• Data preparation: WSI patching, color normalization, augmentation

Day 2 – Model Building

• Choosing architectures (ResNet, VGG, DenseNet, EfficientNet)
• Dataset splitting & validation methods
• Handling class imbalance and selecting evaluation metrics
• Hands-on: Training a CNN model for tissue classification

Day 3 – Optimization & Transfer Learning

• Hyperparameter tuning & regularization methods
• Early stopping and learning rate scheduling
• Transfer learning: pre-trained models & fine-tuning for pathology
• Hands-on: Implementing transfer learning + model interpretability (Grad-CAM)