Pre-Congress Workshops

The access to the Pre-Congress Workshops is free for all registered participants.

Please contact to book a slot a room to meet in your research project during ECDP2023. No costs for academic research projects.

This 90-minute workshop is a hands-on introduction to computational pathology both for pathologists and for computer scientists. Participants can choose according to their expertise between three tracks where they will, in small groups, analyse histological images interactively, using scripted image processing, or using AI techniques:

Whole-Slide Image Analysis with QuPath
In this tutorial “WSI Analysis with QuPath”‚ we will explore the open source software QuPath*, a free to use platform, which provides tools for analysis of scanned histopathological slides, so called whole slide images (WSI). You will learn: (1) How to install QuPath and use it for viewing and annotating a WSI. (2) How to train the AI-based classification algorithm, which is provided by QuPath, so that this algorithm can find tumor and non-tumor areas on a WSI. (3) How to use QuPath to determine the Ki-67 index, a well-known method for assessment of cancer cell proliferation in various tumors. Neither programming skills nor pathology knowledge are needed to participate in this tutorial. Participants should bring their own laptop.

Patch-based WSI analysis: tiling strategies and result-aggregation
In "Patch-based WSI analysis: tiling strategies and result-aggregation", we will look at how to tile a WSI for a patch-based analysis such as Convolutional Neural Networks. We will learn about different strategies for splitting WSI and aggregating the results using a trivial custom segmentation algorithm.
Python knowledge is useful but not mandatory, as the concepts of image processing are the main focus.

Introduction to Training an AI for Histological Image Classification
In this tutorial, we will teach a computer to distinguish between tissue images of two classes. We will guide you through a simple machine learning pipeline including training an algorithm, applying it, and displaying the results. You will learn to interpret the results and how to check them for plausibility in order to avoid common pitfalls of this type of approach. To participate, you should have a google account (we’ll be using a Colab notebook—no other software installation required). There will be a few lines of Python code involved, but no programming or machine learning experience is required.

Please register for this workshop for free here:

Prof. Inti Zlobec, Professor of Digital Pathology – University of Bern, Institute of Tissue Medicine and Pathology
Dr. Bastian Dislich, Staff Pathologist, Institute of Tissue Medicine and Pathology, University of Bern
Joel Ramon Baumann, Medical Student at the University of Bern
Darshan Kumar PhD, Customer Success Manager at Aiforia
Image Analysis is one of AI’s most important use cases today. We’ve recently seen a revolution in the use of AI in medical imaging. Pathologists and machine learning experts play a vital role in this revolution. During this workshop, we discuss how researchers in pathology can build their own AI models and eventually deploy them for clinical use. The workshop also highlights the strengths of AI models in clinical diagnostics and how these models can display superhuman capabilities.
Please register for this workshop for free here:
Find more information about the workshop here:

Péter Gombás MD
Prof. Alessandro Olivo
Tamás Micsik, MD
3DHISTECH guest speakers were pioneers of Digital Pathology and share their experiences & expectations regarding the past, present, and future of our profession.
Please register for this workshop for free here:

Unlock the Power of Digital Pathology: Get an overview of the role and workflow of Digital Pathology in diagnostics and beyond. Experts guide you from selecting the appropriate scanner to the smooth scanning process and using the diagnostic platform with AI modules for your team. You will learn how you organize Digital Pathology across your entire laboratory team and what needs to be considered step by step – including the profitable use of AI modules.
Please register for this workshop for free here:

Diagnostic and therapeutic approaches and therefore biological insights are increasingly dependent on data, which are also being generated, captured and structured more and more. Progress in cancer treatment is established through the application of precision medicine for patients. One approach is the development of AI-assisted algorithms in digital pathology, which relies on highly diverse and high quality de-identified real world data cohorts. In the context of integrated diagnostics, foundational pathology data such as H&E images and associated meta/ clinical data are of great interest in the development of H&E-based algorithms.
To enable further precision medicine tools the exchange of data needs to be seamless, governed by physicians. Industry could support by promoting interoperability based on e.g. the DICOM and HL7 format. The implementation however turns out to be challenging, since technical, physical, sociological and economic barriers need to be overcome. At the same time, AI-based learning diagnostics are increasingly important, based on which it makes sense to embrace the upcoming technology tools.
Current investments in precision medicine support the interoperability between clinicians, business and technology, which can also be used to improve quality of data. Structured and curated real world data cohorts based on the diagnosis and treatment of patients are used for research and development of AI based algorithms.
Agenda, hosted by Roche:
● Workflow & Integration
● Real World Data Cohorts
Registration for this workshop is closed.