AI-Driven Digital Pathology: Histopathology Image Analysis, Cancer Detection & Computational Biomarker Discovery
Transform Histopathology Images into AI-Driven Cancer Insights with Digital Pathology and Explainable Deep Learning
Skills you will gain:
About Program:
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.
Program Objectives:
- 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.
What you will learn?
📅 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
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- 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.
Career Supporting Skills
Program 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.