AI-Driven Digital Pathology: Histopathology Image Analysis, Cancer Detection & Computational Biomarker Discovery

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.

• 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.

• 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.

• 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.