AI-Powered Digital Pathology: Foundation Models, Histopathology Analysis & Cancer Biomarker Discovery
From Tissue Images to Cancer Insights: Hands-On Analysis with AI Foundation Models, Python & Google Colab
Skills you will gain:
About Workshop:
Digital pathology is advancing cancer research by helping researchers analyze histopathology images, recognize cancer-associated tissue patterns, and explore potential molecular biomarkers. This hands-on workshop introduces participants to cutting-edge foundation models, deep learning, computational image analysis, and bioinformatics approaches used in modern cancer research. Through guided practical sessions using Python, Google Colab, and publicly available pathology and genomics datasets, participants will gain experience in tissue image processing, AI-assisted cancer classification, gene expression analysis, and computational biomarker discovery, with a focus on emerging applications in precision oncology.
Aim: To equip participants with practical skills in AI-powered digital pathology and cancer bioinformatics by developing hands-on workflows for histopathology image analysis, cancer-associated tissue classification, and computational biomarker exploration using Google Colab, Python, pretrained AI models, and public cancer research datasets.
Workshop Objectives:
- Understand the fundamentals of digital pathology, histopathology imaging, and AI applications in cancer research.
- Learn to access, preprocess, and analyze histopathology image datasets using Google Colab and Python.
- Explore AI foundation models and machine learning methods for cancer-associated tissue classification.
- Develop practical skills in image segmentation, feature extraction, and model evaluation.
- Investigate cancer genomics, gene expression profiles, and candidate biomarkers using bioinformatics platforms.
- Build a reproducible computational workflow integrating digital pathology analysis and cancer bioinformatics.
What you will learn?
📅 Day 0: Digital Pathology Orientation & Google Colab Setup
- Meet the mentor and explore how AI is transforming digital pathology and cancer research.
- Understand the workshop learning goals, practical activities, and expected outcomes.
- Get familiar with histopathology images, H&E staining, and whole-slide imaging (WSI).
- Explore the role of deep learning, Vision Transformers (ViTs), and pathology foundation models.
- Get an introduction to public research datasets, including PatchCamelyon (PCam), CAMELYON16, and TCGA.
- Set up Google Colab and learn how Python notebooks will be used during the workshop.
- Access the sample datasets, notebooks, and required computational resources.
🧪 Hands-On Activity
Set up Google Colab, run a starter Python notebook, and visualize sample histopathology images. Check GPU availability and ensure the required libraries are working correctly.
Google Colab, Python, NumPy, Matplotlib, PyTorch, Google Drive.
📅 Day 1: Histopathology Image Analysis, Whole-Slide Imaging & Tissue Feature Extraction
- Understand how digital tissue images help researchers study cancer-associated changes in tissue structure.
- Explore normal and tumor-associated tissue patterns using publicly available histopathology datasets.
- Learn how whole-slide images are organized and why image resolution, magnification, and patch selection matter.
- Explore H&E image preprocessing, basic color normalization, and tissue region identification.
- Perform image resizing, quality checks, and patch extraction to prepare tissue images for AI analysis.
- Extract simple morphological and color-based features, and understand their limitations in cancer research.
- Prepare labeled image patches for machine learning while avoiding patient-level data leakage.
🧪 Hands-On Activity
Load and preprocess cancer-related histopathology images using Python and OpenCV. Extract tissue patches, visualize morphological features, and prepare images for AI-based analysis.
Google Colab, Python, OpenCV, OpenSlide (WSI demonstration), Scikit-image, NumPy, Pandas, Matplotlib, PCam Dataset.
📅 Day 2: Pathology Foundation Models, Vision Transformers & AI-Based Cancer Classification
- Discover how pretrained foundation models can identify useful patterns in histopathology images.
- Understand the differences between Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and pathology-specific foundation models.
- Explore representative pathology models such as Phikon, UNI, Virchow, and CONCH, including their research applications and access requirements.
- Learn how image embeddings represent tissue characteristics and support downstream classification.
- Understand transfer learning and why pretrained models are valuable when labeled pathology data are limited.
- Develop a simple AI-based cancer classification workflow using image embeddings.
- Evaluate model performance using confusion matrices, precision, recall, F1-score, and ROC-AUC.
- Explore explainable AI and the role of model interpretation in computational pathology.
🧪 Hands-On Activity
Apply a pretrained image encoder to extract histopathology features and build an AI-based cancer classification model. Evaluate predictions using confusion matrices, F1-score, and ROC-AUC.
Google Colab, Python, PyTorch, Hugging Face Transformers, Pretrained Image Encoder, Scikit-learn, Matplotlib, SHAP or Captum (as appropriate).
📅 Day 3: AI-Driven Cancer Biomarker Discovery, Explainable AI & Precision Oncology
- Understand how tissue morphology may be associated with molecular characteristics and cancer biomarkers.
- Explore the difference between image-based cancer classification and molecular biomarker prediction.
- Learn about genomic alterations, gene expression signatures, and their relevance to precision oncology.
- Discover how pathology-derived image features can be linked with matched molecular data.
- Explore the role of TCGA and the Genomic Data Commons (GDC) in cancer genomics and pathology research.
- Build a simple exploratory biomarker prediction model using labeled research data.
- Apply explainable AI techniques to investigate the features influencing model predictions.
- Understand key challenges including tumor heterogeneity, data bias, model validation, and clinical translation.
- Discuss emerging research directions in multimodal AI, pathology foundation models, and precision oncology.
🧪 Hands-On Activity
Analyze matched pathology-derived features and molecular biomarker data to explore cancer biomarker prediction. Apply SHAP to interpret model predictions and identify potentially informative tissue features.
Google Colab, Python, TCGA/GDC Research Data, Pandas, NumPy, Scikit-learn, SHAP, Matplotlib, Seaborn.
Mentor Profile
Fee Plan
Important Dates
20 Nov 2026 Indian Standard Timing 4:30 PM IST
17 Nov 2026 to 20 Nov 2026 Indian Standard Timing 5:30PM
Get an e-Certificate of Participation!
Intended For :
- Graduate and postgraduate students in Biotechnology, Bioinformatics, Nanotechnology, Pharmacy, Biomedical Sciences, and Life Sciences.
- PhD scholars and researchers working in systems biology, molecular networks, biomarker discovery, and nanomedicine.
- Academicians and faculty members interested in AI-driven bioinformatics, Python-based analysis, and computational nanomedicine.
- Industry professionals in pharmaceutical R&D, biotechnology, molecular diagnostics, AI-based drug discovery, and nanomedicine.
Career Supporting Skills
Workshop Outcomes
- Collect and analyze public histopathology image datasets.
- Perform AI-assisted tissue image processing, segmentation, and feature extraction.
- Apply pretrained pathology foundation-model features to tissue classification.
- Evaluate machine learning models for cancer-associated image classification.
- Explore cancer genomics and gene expression using bioinformatics tools.
- Investigate candidate computational biomarkers and visualize research findings.
- Develop a reproducible Google Colab-based digital pathology workflow.