Bridging the Gap Between Pathology and Computer Science

Learn from the leading voices in computational pathology – real-world insights, machine learning strategies, and the future of data-driven diagnosis. This course is a rare opportunity to learn directly from a group of experts who are actively shaping the future of pathology. Unlike most digital pathology courses led by a single instructor, this series brings together prominent researchers and practitioners in pathology and computer science. You’ll explore real-world AI applications, understand key machine learning concepts, and gain a deep appreciation for how digital tools are transforming tissue diagnostics. If you’re ready to understand not just the “what,” but the “how” and “why” of computational pathology, this course is your bridge.

What You’ll Learn

✔ Foundations of computational pathology and whole slide imaging

✔ Computer vision and deep learning techniques for tissue image analysis

✔ How to handle domain shift, model uncertainty, and interpretability

✔ Real-world strategies for building and evaluating AI models in pathology

Who It’s For

Pathologists, doctors, med students, lab professionals, computer scientists, data scientists, AI engineers, and students exploring interdisciplinary innovation in medicine and AI, especially those who want to understand how computer science is transforming modern pathology.

Why Take This Course?

Unmatched Industry Insight: Learn directly from professionals applying machine learning to real pathology workflows.

Collaborative Intelligence: Gain a balanced understanding of both pathology and computer vision techniques.

Beyond the Basics: Go deeper than surface-level AI buzzwords and explore the real challenges and opportunities in digital diagnostics.

Diverse Expertise: Hear from multiple experts instead of one voice—each module is presented by a different specialist in the field.

By the End You’ll…

✔ Understand key machine learning methods used in computational pathology

✔ Know how to assess model performance, uncertainty, and generalizability

✔ Be familiar with the challenges and solutions in real-world AI deployment

✔ Feel more confident engaging with interdisciplinary digital pathology teams

Course Modules Include

Bridging the Gap between Pathology and Computer Science – BEHIND THE SCENES (uncensored)

The beginnings of computational pathology with Jeroen van der Laak

All about whole slide images w/ Leslie Tessier and Daan Geijs

Computer vision approaches used in tissue image analysis w/ Leander van Eekelen

Deep Learning for Tissue Image Analysis w/ Meyke Hermsen

Weakly supervised deep learning for tissue image analysis w/ Daan Geijs

Unsupervised deep learning tissue image analysis w/ Geert Litjens

Model performance metrics w/ Francesco Ciompi and Leander van Eekelen

How to deal with domain shift in computational pathology? w/ Khrystyna Faryna

Model uncertainty in computational pathology w/ Milda Pocevičiūtė

Intersection between histopathology and spatially resolved gene expression w/ Eduard Chelebian

How to make AI outputs convincing for users in assisted-reading setups w/ Leslie Tessier

Meet the Speakers

Jeroen van der Laak: Professor of Computational Pathology at the Department of Pathology at the Radboud University Medical Center in Nijmegen & guest professor at the Center for Medical Image Science and Visualization (CMIV) in Linkoping, Sweden. Research focus: Improving cancer diagnostics and prognostics with machine learning and large data sets in pathology.

Geert Litjens: Assistant Professor at Radboud University Nijmegen Medical Center. Research focus: Application of modern machine learning methods to oncological pathology (focus on prostate and pancreatic cancer).

Francesco Ciompi: Assistant Professor of Computational Pathology at Radboud University Medical Center, Nijmegen. Research focus: AI in precision oncology, computer-aided diagnosis for large-scale digital pathology and multi-modal data.

Daan Geijs: PhD candidate in the Computational Pathology Group at Radboud University Nijmegen Medical Center. Research focus: Implementing deep learning in the daily routine of dermatopathologists.

Leslie Tessier PhD candidate in the Computational Pathology Group at Radboud University Nijmegen Medical Center & Resident Physician (Pathology), CHU Angers, France. Research focus: Automated assessment of tubule formation in breast cancer.

Leander van Eekelen: PhD candidate in the Computational Pathology Group at Radboud University Nijmegen Medical Center. Research focus: Improving lung cancer immunotherapy with deep learning.

Meyke Hermsen: Study manager and PhD candidate in the Computational Pathology Group at Radboud University Nijmegen Medical Center. Research focus: Deep learning applications for renal transplant pathology.

Khrystyna Faryna: PhD candidate in the Computational Pathology Group at Radboud University Nijmegen Medical Center. Research focus: Bridging the clinical integration gap for deep learning–based methods in computational pathology by improving model generalization.

Eduard Chelebian: PhD candidate in the Department of Information Technology at Uppsala University, Sweden. Research focus: How deep learning networks learn – intermediate representations of convolutional neural networks in histopathological imaging.

Milda Pocevičiūtė PhD candidate in the Computer Graphics and Image Processing Group at Linköping University, Sweden. Research focus: Explainable artificial intelligence (XAI), anomaly detection and uncertainty techniques for digital pathology.k

“The best way to understand how computational pathology is changing diagnostics is to learn directly from the industry experts.”

– Aleksandra Zuraw, DVM, Ph.D., Dipl. ACVP

Designed for Pathology Minds Like Yours

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