AI-Powered Digital Pathology: Foundation Models, Cancer Detection & Computational Biomarkers

• Introduce the fundamentals of digital pathology and computational histopathology.
• Develop practical understanding of histopathology image preprocessing and patch extraction.
• Train participants to build and evaluate CNN-based cancer classification models.
• Introduce Vision Transformers and foundation models for biomedical image analysis.
• Demonstrate the use of pretrained models and transfer learning for pathology applications.
• Develop skills in tumor-region identification and probability-map generation.
• Introduce computational biomarker discovery using deep-learning-derived features.
• Apply explainable AI techniques such as Grad-CAM to interpret model predictions.
• Teach participants how to evaluate biomedical AI models using appropriate performance metrics.
• Build awareness of bias, reproducibility, interpretability, and clinical translation challenges in AI-powered pathology.

📅 Day 1: Digital Pathology Data Preparation & Histopathology Image Analysis

Focus: Preparing pathology image data for AI-based cancer analysis.

Key Topics

• Introduction to digital pathology and computational histopathology
• Whole-slide images and public pathology datasets
• Histopathology image preprocessing
• Tissue-region and ROI identification
• Patch extraction and image normalization
• Image augmentation
• Preparation of cancer and non-cancer datasets

🛠️ Hands-on

• Explore public histopathology images
• Load and visualize pathology images
• Perform image preprocessing and normalization
• Extract tissue patches
• Apply image augmentation
• Prepare a structured dataset for AI modelling

🧰 Tools

Python | Google Colab | OpenCV | OpenSlide | NumPy | Pandas | scikit-image | TCGA Pathology Images

📅 Day 2: Deep Learning & Foundation Models for Cancer Detection

Focus: Building and evaluating AI models for histopathology image classification.

Key Topics

• Deep learning for biomedical image analysis
• CNN-based cancer classification
• Transfer learning with pretrained models
• Feature extraction from pathology images
• Vision Transformers
• Biomedical foundation models
• Model training and validation
• Accuracy, precision, recall, F1-score, ROC and AUC

🛠️ Hands-on

• Build a CNN-based classification model
• Apply transfer learning
• Extract image-level deep features
• Explore a pretrained Vision Transformer
• Evaluate model performance
• Compare different AI approaches

🧰 Tools

Python | PyTorch | torchvision | MONAI | Hugging Face | scikit-learn | Google Colab

📅 Day 3: Computational Biomarkers & Explainable AI

Focus: Interpreting predictions and deriving clinically relevant information from AI models.

Key Topics

• Tumor-region identification
• Patch-level and patient-level predictions
• Computational biomarker concepts
• Deep-feature aggregation
• Explainable AI in pathology
• Grad-CAM visualization
• Model interpretability and bias
• Patient-level cancer classification

🛠️ Hands-on

• Generate tumor-probability predictions
• Identify high-confidence tumor regions
• Create Grad-CAM heatmaps
• Extract deep-learning features
• Compare correct and incorrect predictions
• Aggregate predictions at patient level
• Build an interpretable digital pathology workflow

🧰 Tools

Python | PyTorch | MONAI | OpenCV | Hugging Face | Grad-CAM | scikit-learn | Matplotlib | TCGA Pathology Images

• Understand the complete digital-pathology AI workflow from image acquisition to prediction.
• Retrieve and work with publicly available histopathology datasets.
• Preprocess pathology images and extract suitable tissue patches for AI analysis.
• Prepare structured datasets for cancer classification.
• Build a basic CNN-based histopathology classification model.
• Apply transfer learning using pretrained deep-learning models.
• Understand the role of Vision Transformers and foundation models in digital pathology.
• Evaluate cancer-classification models using appropriate biomedical AI metrics.
• Identify tumor-associated regions using model-generated probability scores.
• Generate Grad-CAM visualizations to understand which tissue regions influence AI predictions.
• Extract deep-learning features that can be explored as computational biomarkers.
• Aggregate image-patch predictions into patient-level results.
• Recognize potential sources of bias, overfitting, and poor model generalization.
• Design an interpretable end-to-end AI workflow for digital pathology research.
• Apply the learned concepts to academic projects, dissertations, research studies, and early-stage biomedical AI applications.

• Graduate Students in Biotechnology, Bioinformatics, Life Sciences, Biomedical Sciences, Computer Science, AI, Data Science, Pharmacy, and related disciplines.
• Postgraduate Students pursuing M.Sc., M.Tech., M.Pharm., M.Biotech., M.Sc. Bioinformatics, Biomedical Engineering, Data Science, AI, or related programs.
• Ph.D. Scholars and Researchers working in cancer biology, pathology, biomedical imaging, computational biology, bioinformatics, artificial intelligence, or healthcare research.
• Academicians and Faculty Members seeking to introduce digital pathology, biomedical AI, or medical-image analysis into teaching and research.
• Industry Professionals working in biotechnology, diagnostics, healthcare AI, pharmaceutical research, medical imaging, pathology, health-tech, and biomedical data science.