# Event Details - **Event Name**: The Diagnexia Computational Pathology Course 2026 - **Event Start and End Date**: Tue, 18 Aug, 2026 at 09:00 am – Wed, 19 Aug, 2026 at 06:00 pm - **Event Description**: Explore AI, image analysis, and deep learning in pathology. A two-day intensive course on computational techniques transforming diagnostics.About this EventDiagnexia Computational Pathology Course? Queens College, Oxford? Date: August 18 and 19 2026August 18th Accomodation Included With Ticket PurchaseGet full access to both the Symposium and the Computational Pathology Course at a discounted rate.Bundle price includes:Consultant discounted rate: £475 (previous rate for both £560)Trainee rate: £327 (previous rate for both £385)Limited availability.Day 109:30 – 10:00 Registration & CoffeeSign-in, materials collection, networking10:00 – 10:30 - Welcome & Introduction to Computational Pathology - RC + PMCourse overview and learning objectives; definition and scope of computational pathology; distinction from digital pathology; the data-intensive nature of pathology; course roadmap10:30 – 11:15 - Digital Image Fundamentals I: Core Concepts - PMWhat 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 downstream11:15 – 11:30 - Coffee Break11:30 – 12:15 - Digital Image Fundamentals II: Whole-Slide Imaging - PMWhole-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 pipelines12:15 – 13:00 - Stain Physics and Color Analysis - AJPhysics 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 challengesLunch - 13:00 – 14:0014:00 – 14:45 - Image Processing Fundamentals - AJIntensity transformations; histogram processing; spatial filtering (smoothing and sharpening); morphological operations (erosion, dilation, opening, closing); practical applications in tissue analysis14:45 – 15:30 - Segmentation in Pathology - PMTissue vs. background detection; thresholding methods (global, Otsu’s, adaptive); region-based and edge-based approaches; nuclear and cell segmentation; the reference area problem15:30 – 15:45 - Coffee Break15:45 – 16:30 - Feature Extraction: Classical Approaches - AJMorphological features (size, shape, texture); nuclear and cellular descriptors; glandular and architectural features; tumor microenvironment metrics; building interpretable feature sets16:30 – 17:15 - Introduction to Machine Learning for Pathology - AJSupervised vs. unsupervised learning; training, validation, and test sets; classical methods (random forests, SVM); performance metrics and their clinical interpretation; avoiding common pitfalls17:15 – 18:00 - Introduction to Deep Learning - AJNeural network fundamentals; forward propagation and backpropagation (conceptual); activation functions; loss functions and optimization; regularisation techniques; training vs. inference; why deep learning transformed computational pathology18:00 – 18:15 - Day 1 Wrap-up and Q&ASummary of key concepts; preview of Day 2; open questionsDay 2 08:30 – 09:00 Coffee & Networking09:00 – 09:15 - Day 1 Recap and Day 2 Overview - PMBrief review of foundational concepts; outline of advanced topics09:15 – 10:00 - Deep Learning Architectures for Pathology - EKConvolutional neural networks (CNNs) explained; landmark architectures (ResNet, U-Net); Vision Transformers (ViT); classification, segmentation, and detection networks; transfer learning and domain adaptation10:00 – 10:45 - Training Strategies and Data Challenges - AJData preparation and patch extraction; annotation strategies and quality; handling class imbalance; data augmentation (geometric, color, stain); managing staining and scanner variability10:45 – 11:00 - Coffee Break11:00 – 11:45 - Multiple Instance Learning and Slide-Level Prediction - JWThe MIL paradigm for whole-slide analysis; attention-based aggregation; weakly supervised learning; from patches to patient-level predictions11:45 – 12:30 - Foundation Models and Vision-Language Models - EKSelf-supervised and contrastive learning; pathology foundation models (overview); vision-language models (CLIP, PathChat, CONCH); zero-shot classification; conversational AI for pathology; limitations and hallucination risks12:30 – 12:45 - Morning Q&ALunch - 12:45 – 13:4513:45 – 14:30 - Clinical Applications I: Diagnostic Support - AJCancer detection and classification (breast, prostate, colorectal, lung); Gleason grading automation; metastasis detection; quality control and triage applications; discussion on clinical utility14:30 – 15:15 - Clinical Applications II: Biomarkers and Prognosis - JWIHC 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 utility15:15 – 15:30 - Coffee Break15:30 – 16:00 - Validation, Regulation, and Deployment - JAEvaluation metrics for classification, segmentation, detection; cross-validation and external validation; regulatory pathways (FDA, CE/MDR); algorithmic fairness and bias considerations; clinical integration and continuous monitoring16:00 – 16:30 - Model Interpretability and Explainable AI - EKWhy interpretability matters in clinical pathology; XAI techniques (saliency maps, Grad-CAM, attention visualization); accuracy vs. interpretability trade-offs; building pathologist trust16:30 – 17:00 - Emerging Technologies - AJMultiplex imaging analysis (mIF, IMC, spatial transcriptomics); multimodal data integration; federated learning; AI-augmented pathology workflows17:00 – 17:30 - Future Directions and the Pathologist’s Role - PMThe 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 development17:30 – 18:00 - Open Discussion, Q&A, and ClosingPanel discussion; participant questions; key takeaways; resources for continued learning; course evaluation; closing remarksSpeakers abbreviationsRC - Prof. Runjan Chetty Chief Medical Officer at Deciphex/DiagnexiaEK - Emre Köse Computational Pathologist @ DeciphexAJ - Andrew Janowczyk Assistant Professor at Emory UniversityJW - John Weldon Clinical AI Director @ DeciphexJA - Jonathan Armstrong AI Governance Lead @ DeciphexPM - Pierre Moulin MD, PhD — Chief Scientific Officer @ Deciphex - **Event URL**: https://allevents.in/oxford/the-diagnexia-computational-pathology-course-2026/100001985726778248 - **Event Categories**: workshops, business, photography - **Interested Audience**: - total_interested_count: 0 - **Event Highlights**: - Duration: 9 hours - Location: The Queen's College - Languages: English ## Ticket Details - **Ticket Price Range**: min: 119.89, max: 515.32, currency: GBP ## Event venue details - **city**: Oxford - **state**: EN - **country**: United Kingdom - **location**: The Queen's College - **lat**: 51.7527629 - **long**: -1.2510199 - **full address**: The Queen's College, High Street, Oxford, United Kingdom ## Event gallery - **Alt text**: The Diagnexia Computational Pathology Course 2026 - **Image URL**: https://cdn-az.allevents.in/events6/banners/8468e847a3f49399d9d65cde1651faa7e6425dcdfb6273695f9b64b066799ca2-rimg-w915-h511-dc010001-gmir.jpg?v=1774285391 ## FAQs - **Q**: When and where is The Diagnexia Computational Pathology Course 2026 being held? - **A:** The Diagnexia Computational Pathology Course 2026 takes place on Tue, 18 Aug, 2026 at 09:00 am to Wed, 19 Aug, 2026 at 06:00 pm at The Queen's College, High Street, Oxford, United Kingdom. - **Q**: Who is organizing The Diagnexia Computational Pathology Course 2026? - **A:** The Diagnexia Computational Pathology Course 2026 is organized by Diagnexia UK Ltd. - **Q**: Who is this event for? Is it right for me? - **A:** The Diagnexia Computational Pathology Course 2026 is ideal for curious learners, students, and skill-builders looking to gain hands-on knowledge and practical expertise in a focused, interactive setting. Whether you're a first-time attendee or a longtime enthusiast in Oxford, this event is thoughtfully curated to deliver a standout experience worth every moment. 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