Deep Learning for Histopathology Image Analysis

Histopathology remains the gold standard for disease diagnosis, especially in cancer. With the digitization of pathology slides, deep learning has emerged as a powerful tool to analyze tissue morphology at scale, enabling reproducible and objective decision support for pathologists. Convolutional neural networks and modern AI architectures now play a central role in tumor detection, grading, and outcome prediction. This workshop provides a structured introduction to histopathology image analysis using deep learning, covering image preprocessing, patch-based learning, classification, and model evaluation. Through hands-on dry-lab sessions, participants will work with real histopathology datasets and learn best practices for performance assessment and explainability, preparing them for research or industry roles in digital pathology.

This workshop aims to train participants in applying deep learning techniques to histopathology images for disease detection, classification, and biomarker analysis. It focuses on computational (dry-lab) workflows for handling large pathology images, feature extraction, and model development. Participants will gain practical skills in building, evaluating, and interpreting deep learning models for medical image analysis. The program bridges AI research with real-world digital pathology applications.

Day 1 – Introduction to Histopathology Imaging and AI Workflows

• Introduction to Histopathology Imaging and AI Workflows
• Data Preprocessing, Augmentation & Normalization
• Image Segmentation and Annotation Techniques

Day 2 – Building Deep Learning Models for Tumor Detection

• Deep Learning Architectures for Tumor Detection
• Dataset Splitting, Validation & Evaluation Metrics
• Hands-on: Applying U-Net / Mask R-CNN for Segmentation

Day 3 – Model Optimization and Transfer Learning

• Model Optimization & Transfer Learning
• Hyperparameter Tuning & Regularization Techniques
• Hands-on: Grad-CAM for Model Interpretability