AI for Quantitative Pathology and Biomarker Analysis
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Course Overview
Program Syllabus
Module 1
About This Course
Quantitative pathology and biomarker analysis play a critical role in precision medicine, enabling objective assessment of tissue and cellular features to inform disease diagnosis and treatment. Traditional manual analysis is often time-consuming and subject to variability. AI techniques, including machine learning and deep learning, provide scalable, accurate, and reproducible methods for quantitative analysis of histopathology images. This workshop will cover AI-driven approaches for automated tissue segmentation, feature extraction, and biomarker quantification. Participants will also explore how AI models can correlate image-derived features with clinical and molecular data to discover and validate novel biomarkers. Real-world case studies will demonstrate the impact of AI in improving disease diagnosis and personalized treatment planning.
Module 2
Aim
This workshop aims to introduce participants to the application of Artificial Intelligence (AI) in quantitative pathology and biomarker analysis. Participants will learn how AI can automate image analysis, quantify tissue and cellular features, and identify disease biomarkers to improve diagnostics, prognostics, and personalized medicine strategies.
Module 3
Workshop Objectives
- Understand the principles of quantitative pathology and biomarker analysis.
- Learn how AI can automate tissue segmentation and feature quantification.
- Gain hands-on experience with deep learning models for image analysis.
- Explore AI-driven approaches for biomarker discovery and validation.
- Apply AI techniques to integrate pathology and clinical data for personalized medicine.
Module 4
Workshop Structure
Day 1: Biomarkers & Image Feature
- Assays & scoring: IHC/ISH/mIF; H-score, Allred, CPS/TPS.
- WSI pipeline: scanning, tiling, TMAs vs WSIs, formats; data governance & PHI.
- Stains & QC: color normalization (Macenko/Reinhard), deconvolution; artifact/tissue detection.
- Segmentation & features: nuclei/cell/region; thresholding/watershed/StarDist/Hover-Net; morphology, intensity (DAB OD), texture (GLCM), spatial (Ripley’s K).
- Hands-on: QuPath navigation/ROI, deconvolution; CellProfiler nuclei features → export.
- Mini-task: compute H-score on patches across cases.
Day 2: AI Models for Quantifying Biomarker Expression
- Data & prep: patient-level splits, leakage control, stratification, imbalance fixes; tiling/augmentation, stain-aware transforms.
- Models: tabular ML (LR/RF/XGB), CNNs on tiles, MIL/attention for WSIs.
- Labels & scoring: pathologist/proxy/consensus, active learning; automated H-score/Allred, PD-L1 CPS/TPS; tumor vs stroma.
- Eval & robustness: patient-level metrics, calibration, error analysis; cross-site/stain variability, domain adaptation; tracking/audit.
- Hands-on: baseline on features, fine-tune CNN, simple MIL; per-case scores.
- Mini-task: compare classical vs deep models + calibration.
Day 3: Statistics, Visualization & Clinical Translation
- Stats: group tests, covariate correlations, survival (KM/Cox); ROC/PR, thresholds, decision curves.
- Visualization: heat/attention maps, Grad-CAM, spatial positivity maps.
- Reporting & clinical: notebooks/figures/tables; pathologist-in-loop, LIS/PACS fit, CAP/CLIA.
- Deployment: packaging, batch vs real-time, sizing, monitoring, bias/fairness & drift; SOPs, validation, change control.
- Hands-on: cohort summaries, survival curves, explainability overlays; 1-page validation report.
- Mini-task: clinician-readable score report with interpretation.
Module 5
Who Should Enrol?
- Undergraduate/Postgraduate Degree in Bioinformatics, Biotechnology, Computational Biology, Biomedical Engineering, or related fields.
- Professionals in pathology, clinical research, oncology, or medical imaging.
- Data Scientists and AI/ML Engineers interested in applying AI to pathology and biomarker analysis.
- Individuals with a strong interest in AI applications in healthcare and precision medicine.
Module 6
Important Dates
Module 7
Registration Ends
10/14/2025
IST 7:00 PM
Module 8
Workshop Dates
10/14/2025 – 10/16/2025
IST 8:00 PM
Module 9
Workshop Outcomes
- Ability to apply AI for quantitative analysis of histopathology images.
- Skills in automating tissue segmentation and cellular feature extraction.
- Hands-on experience in AI-based biomarker discovery and validation.
- Understanding of integrating image-derived features with clinical and molecular data.
- Knowledge of AI applications in precision medicine and oncology.
- Enhanced ability to interpret AI outputs for clinical decision-making.
Module 10
Fee Structure
Module 11
Student Fee
₹1499 | $55
Module 12
Ph.D. Scholar / Researcher Fee
₹2499 | $65
Module 13
Academician / Faculty Fee
₹3499 | $80
Module 14
Student Fee
₹4499 | $90
Module 15
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Module 16
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Module 17
Publication Opportunity
Get published in a prestigious open-access journal.
Module 18
Centre of Excellence
Become part of an elite research community.
Module 19
Networking & Learning
Connect with global researchers and mentors.
Module 20
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
Module 21
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Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
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AI for Quantitative Pathology and Biomarker Analysis
Professional Certification Program
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FormatLive + Recorded
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Duration8 Weeks
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CertificationVerified
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