Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Quantitative Pathology and Biomarker Analysis shows how machine learning turns the subjective art of reading tissue into reproducible measurement. You learn to work with whole-slide digital pathology images and apply deep learning to segment tissue and cells, quantify biomarkers such as protein expression and tumour features, and support diagnosis and grading. The course covers the practical realities — stain variation, annotation, validation and clinician trust — and connects quantification to precision pathology. You finish able to reason about an AI quantitative-pathology workflow. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI for quantitative pathology and biomarker analysis — analysing digital pathology images to quantify tissue features and biomarkers for research and diagnosis.
📋 Course Objectives
1. Work with whole-slide digital pathology images.
2. Segment tissue and cells with deep learning.
3. Quantify biomarkers and tissue features.
4. Support diagnosis, grading and scoring.
5. Address stain variation and validation.
👥 Who Should Enroll?
• Pathologists and biomedical researchers
• Digital-pathology and imaging scientists
• Biotech and diagnostics teams
• Students of computational pathology
🚀 Key Learning Outcomes
• An understanding of AI in quantitative pathology.
• A biomarker-quantification perspective.
• A validation-focused approach.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Foundations of Quantitative Pathology & Imaging
Understand various biomarker assays like IHC/ISH/mIF and scoring methods (H-score, Allred, CPS/TPS). • Explore the Whole Slide Imaging (WSI) pipeline, including scanning, tiling, formats, and data governance. • Apply image quality control techniques such as color normalization and artifact detection.
Advanced Image Segmentation & Feature Extraction
Implement segmentation techniques for nuclei, cells, and regions using methods like thresholding, watershed, StarDist, and Hover-Net. • Extract diverse features including morphology, intensity (DAB OD), texture (GLCM), and spatial correlations (Ripley's K). • Navigate QuPath for ROI definition and deconvolution, and CellProfiler for nuclei feature extraction.
AI Model Development for Biomarker Quantification
Prepare data effectively, managing patient-level splits, leakage, stratification, and class imbalance. • Utilize tabular ML models (LR/RF/XGB), CNNs on tiles, and Multiple Instance Learning (MIL) for WSIs. • Develop automated scoring systems for H-score, Allred, and PD-L1 CPS/TPS.
Model Evaluation and Robustness
Evaluate models using patient-level metrics, calibration, and error analysis. • Address cross-site and stain variability and implement domain adaptation strategies. • Conduct hands-on fine-tuning of CNNs, simple MIL implementations, and performance comparison of classical vs. deep models.
Statistical Analysis and Visualization in Pathology
Perform statistical tests including group comparisons, covariate correlations, and survival analysis (KM/Cox). • Interpret ROC/PR curves, thresholds, and decision curves for model assessment. • Generate visual explanations using heatmaps, attention maps, Grad-CAM, and spatial positivity maps.
Clinical Translation and Deployment of AI Models
Prepare comprehensive reports for clinical translation, including notebooks, figures, and tables. • Understand integration with LIS/PACS and compliance with CAP/CLIA regulations. • Implement deployment strategies for packaging, batch vs. real-time processing, monitoring, and bias/fairness considerations.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | QuPath |
| Covered Tool / Platform | CellProfiler |
| Covered Tool / Platform | Logistic Regression |
| Covered Tool / Platform | Random Forest |
| Covered Tool / Platform | XGBoost |
| Covered Tool / Platform | CNNs |
| Covered Tool / Platform | MIL |
| Covered Tool / Platform | Grad-CAM |
| Covered Tool / Platform | Kaplan-Meier |
| Covered Tool / Platform | Cox Models |
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