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Computational Pathology Fundamentals

2-Day Course Agenda

Duration: 2 Days  |  Date: 18-19 August 2026

About the Course

Computational Pathology Fundamentals

Computational pathology and artificial intelligence are rapidly becoming part of modern diagnostic practice. This intensive 2-day course is designed specifically for pathologists and trainees who want to understand how these technologies work, how to critically evaluate them, and how they can be applied in clinical and research settings. The programme has been developed to bridge the gap between surface-level discussions of AI and the practical knowledge needed to engage confidently with computational pathology. No prior computational or programming experience is required.

Across two days of expert-led teaching, attendees will explore the foundations of digital imaging, whole-slide imaging, image analysis, machine learning, and deep learning. Through lectures, demonstrations, discussions, and practical examples, participants will gain a structured understanding of how computational tools are already being used for biomarker quantification, cancer grading, prognosis prediction, and clinical decision support. The course also addresses validation, regulation, and implementation considerations, helping attendees understand both the opportunities and limitations of AI in pathology.

Whether you are exploring digital pathology for the first time or looking to deepen your understanding of computational approaches, you will leave with a strong practical foundation and the confidence to engage with one of the fastest-evolving areas of modern pathology.

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This course is accredited for 14 CPD points from The Royal College of Pathologists

DAY 1: Foundations of Computational Pathology

Time
Session
Description
Morning Session
09:30 – 10:00
Registration & Coffee
Sign-in, materials collection, networking
10:00 – 10:30
Welcome & Introduction to Computational Pathology
Prof. Runjan Chetty Chief Medical Officer, Deciphex/Diagnexia
Pierre Moulin MD, PhD - Chief Scientific Officer, Deciphex
Course overview and learning objectives; definition and scope of computational pathology; distinction from digital pathology; the data-intensive nature of pathology; course roadmap
10:30 – 11:15
Digital Image Fundamentals I: Core Concepts
Pierre Moulin MD, PhD - Chief Scientific Officer, Deciphex
What is a digital image; pixels, resolution, and bit depth; color spaces (RGB, HSV, LAB); relationship between magnification and microns per pixel (mpp); image file formats (JPEG, PNG, TIFF); lossy vs. lossless compression and their impact on analysis; why format choices matter downstream
11:15 – 11:30
Coffee Break
11:30 – 12:15
Digital Image Fundamentals II: Whole-Slide Imaging
Pierre Moulin MD, PhD - Chief Scientific Officer, Deciphex
Whole-slide imaging technology; scanner types and acquisition; pyramidal/multi-resolution image structure; proprietary vs. open WSI formats (SVS, NDPI, DICOM WSI); storage, memory, and bandwidth considerations; practical implications for computational pipelines
12:15 – 13:00
Stain Physics and Color Analysis
Andrew Janowczyk Assistant Professor, Emory University
Physics of stain absorption (Beer-Lambert law); H&E and special stains from a computational perspective; stain vectors and color deconvolution; stain normalization approaches; quantification of chromogenic and fluorescent signals; inter-laboratory variability challenges
13:00 – 14:00
Lunch Break
Afternoon Session
14:00 – 14:45
Image Processing Fundamentals
Andrew Janowczyk Assistant Professor, Emory University
Intensity transformations; histogram processing; spatial filtering (smoothing and sharpening); morphological operations (erosion, dilation, opening, closing); practical applications in tissue analysis
14:45 – 15:30
Segmentation in Pathology
Pierre Moulin MD, PhD - Chief Scientific Officer, Deciphex
Tissue vs. background detection; thresholding methods (global, Otsu’s, adaptive); region-based and edge-based approaches; nuclear and cell segmentation; the reference area problem
15:30 – 15:45
Coffee Break
15:30 – 15:45
Feature Extraction: Classical Approaches
Andrew Janowczyk Assistant Professor, Emory University
Morphological features (size, shape, texture); nuclear and cellular descriptors; glandular and architectural features; tumor microenvironment metrics; building interpretable feature sets
14.30-15.00
Introduction to Machine Learning for Pathology
Andrew Janowczyk Assistant Professor, Emory University
Supervised vs. unsupervised learning; training, validation, and test sets; classical methods (random forests, SVM); performance metrics and their clinical interpretation; avoiding common pitfalls
15.05-15.35
Introduction to Deep Learning
Neural network fundamentals; forward propagation and backpropagation (conceptual); activation functions; loss functions and optimization; regularisation techniques; training vs. inference; why deep learning transformed computational pathology
15.40-16.10
Day 1 Wrap-up and Q&A
Summary of key concepts; preview of Day 2; open questions
28
Sponsoring the Diagnexia Histopathology Symposium 2023

DAY 2: Advanced Topics and Clinical Applications

Time
Session
Description
Morning Session
08:30 – 09:00
Coffee & Networking
09:00 – 09:15
Day 1 Recap and Day 2 Overview - PM
Pierre Moulin MD, PhD - Chief Scientific Officer, Deciphex
Brief review of foundational concepts; outline of advanced topics
09:15 – 10:00
Deep Learning Architectures for Pathology
Emre Köse Computational Pathologist, Deciphex
Convolutional neural networks (CNNs) explained; landmark architectures (ResNet, U-Net); Vision Transformers (ViT); classification, segmentation, and detection networks; transfer learning and domain adaptation
10:00 – 10:45
Training Strategies and Data Challenges
Andrew Janowczyk Assistant Professor, Emory University
Data preparation and patch extraction; annotation strategies and quality; handling class imbalance; data augmentation (geometric, color, stain); managing staining and scanner variability
10:45 – 11:00
Coffee Break
11:00 – 11:45
Multiple Instance Learning and Slide-Level Prediction
John Weldon Clinical AI Director, Deciphex
The MIL paradigm for whole-slide analysis; attention-based aggregation; weakly supervised learning; from patches to patient-level predictions
11:45 – 12:30
Foundation Models and Vision-Language Models
Emre Köse Computational Pathologist, Deciphex
Self-supervised and contrastive learning; pathology foundation models (overview); vision-language models (CLIP, PathChat, CONCH); zero-shot classification; conversational AI for pathology; limitations and hallucination risks
12:30 – 12:45
Morning Q&A
12:45 – 13:45
Lunch Break
Afternoon Session
13:45 – 14:30
Clinical Applications I: Diagnostic Support
Andrew Janowczyk Assistant Professor, Emory University
Cancer detection and classification (breast, prostate, colorectal, lung); Gleason grading automation; metastasis detection; quality control and triage applications; discussion on clinical utility
14:30 – 15:15
Segmentation in Pathology
John Weldon Clinical AI Director, Deciphex
IHC quantification (Ki-67, HER2, PD-L1); tumor microenvironment analysis; TIL assessment; predicting molecular features from H&E; survival and treatment response prediction; discussion on clinical utility
15:15 – 15:30
Coffee Break
15:30 – 16:00
Validation, Regulation, and Deployment
Jonathan Armstrong AI Governance Lead, Deciphex
Evaluation metrics for classification, segmentation, detection; cross-validation and external validation; regulatory pathways (FDA, CE/MDR); algorithmic fairness and bias considerations; clinical integration and continuous monitoring
16:00 – 16:30
Model Interpretability and Explainable
Andrew Janowczyk Assistant Professor, Emory University
Emre Köse Computational Pathologist, Deciphex
Why interpretability matters in clinical pathology; XAI techniques (saliency maps, Grad-CAM, attention visualization); accuracy vs. interpretability trade-offs; building pathologist trust
16:30 – 17:00
Future Directions and the Pathologist’s Role
Andrew Janowczyk Assistant Professor, Emory University
Multiplex imaging analysis (mIF, IMC, spatial transcriptomics); multimodal data integration; federated learning; AI-augmented pathology workflows
17:00 – 17:30
Emerging Technologies
Pierre Moulin MD, PhD - Chief Scientific Officer, Deciphex
The pathologist’s role in computational pathology development; integration of imaging with -omics data; AI-assisted reporting and case management; building multidisciplinary collaborations; career pathways and skill development
17:30 – 18:00
Open Discussion, Q&A, and Closing
Panel discussion; participant questions; key takeaways; resources for continued learning; course evaluation; closing remarks
28

FAQ

Answers to some of our most frequently asked questions. For further information, download our FAQ brochure below.

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DON’T MISS OUT

Don’t miss the 2026 Diagnexia Computational Pathology Fundamentals course — a rare opportunity to learn directly from leading experts, sharpen diagnostic skills, and exchange insights with peers in the historic setting of The Queen’s College, Oxford.

Spaces are limited — register now to secure your place.

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