The Diagnexia Computational Pathology Course 2026

📍 Queens College, Oxford
📅 Date: August 18 and 19 2026

Day 1

09:30 – 10:00 Registration & Coffee

Sign-in, materials collection, networking

10:00 – 10:30 - Welcome & Introduction to Computational Pathology - RC + PM

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 - PM

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 - PM

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 - AJ

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

Lunch - 13:00 – 14:00

14:00 – 14:45 - Image Processing Fundamentals - AJ

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 - PM

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

15:45 – 16:30 - Feature Extraction: Classical Approaches - AJ

Morphological features (size, shape, texture); nuclear and cellular descriptors; glandular and architectural features; tumor microenvironment metrics; building interpretable feature sets

16:30 – 17:15 - Introduction to Machine Learning for Pathology - AJ

Supervised vs. unsupervised learning; training, validation, and test sets; classical methods (random forests, SVM); performance metrics and their clinical interpretation; avoiding common pitfalls

17:15 – 18:00 - Introduction to Deep Learning - AJ

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

18:00 – 18:15 - Day 1 Wrap-up and Q&A

Summary of key concepts; preview of Day 2; open questions

Day 2

08:30 – 09:00 Coffee & Networking

09:00 – 09:15 - Day 1 Recap and Day 2 Overview - PM

Brief review of foundational concepts; outline of advanced topics

09:15 – 10:00 - Deep Learning Architectures for Pathology - EK

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 - AJ

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 - JW

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 - EK

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

Lunch - 12:45 – 13:45

13:45 – 14:30 - Clinical Applications I: Diagnostic Support - AJ

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 - Clinical Applications II: Biomarkers and Prognosis - JW

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 - JA

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 AI - EK

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 - Emerging Technologies - AJ

Multiplex imaging analysis (mIF, IMC, spatial transcriptomics); multimodal data integration; federated learning; AI-augmented pathology workflows

17:00 – 17:30 - Future Directions and the Pathologist’s Role - PM

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

Speakers abbreviations

RC - Prof. Runjan Chetty Chief Medical Officer at Deciphex/Diagnexia

EK - Emre Köse Computational Pathologist @ Deciphex

AJ - Andrew Janowczyk Assistant Professor at Emory University

JW - John Weldon Clinical AI Director @ Deciphex

JA - Jonathan Armstrong AI Governance Lead @ Deciphex

PM - Pierre Moulin MD, PhD — Chief Scientific Officer @ Deciphex

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Host or Publisher Diagnexia UK Ltd