About Workshop
This 3-day hands-on workshop provides a structured introduction to AI-powered digital pathology for cancer research and computational biomarker discovery. participants will gain practical experience in histopathology image preprocessing, deep-learning-based cancer classification, Vision Transformers, biomedical foundation models, tumor-region identification, and explainable AI techniques such as Grad-CAM. Using publicly available pathology datasets and industry-relevant tools including Python, Google Colab, PyTorch, MONAI, OpenCV, Hugging Face, OpenSlide, and scikit-learn, participants will develop an end-to-end digital pathology workflow applicable to academic research, biomedical AI projects, cancer informatics, and emerging healthcare applications.
Aim
To equip participants with practical knowledge of digital pathology and artificial intelligence for analyzing histopathology images, detecting cancer-associated patterns, developing computational biomarkers, and interpreting deep-learning predictions using explainable AI techniques.
What Participants Will Learn
- 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.
Structure
📅 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
Important Dates
Registration Ends
4: 30 PM
Workshop Dates
2026-09-10
5:30 PM
5:30 PM
What You Will Gain
Outcomes
- 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.
Who Should Attend
- 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.