Scientific Program

09:00 Frederick Klauschen Germany
Welcome words from the Congress President
09:05 Norman Zerbe Germany
Welcome words from ESDIP
09:10 Klaus-Robert Müller Germany
Artificial Intelligence in Pathology and beyond

Gunter Haroske Germany
Sabine Leh Norway

09:30 Iris Nagtegaal The Netherlands
Datasets and structured reporting: the future of pathology?
09:55 Andrea Essenwanger (Germany) et al.
Specifying a Pathology Structured Report with HL7 FHIR
10:05 Sabine Leh (Norway) et al.
Kidney Biopsy Codes: a multi-hierarchical terminology for non-neoplastic kidney biopsies

Johan Lundin Finland
Yuri Tolkach Germany

10:45 Peter Boor Germany
Deep learning applications in kidney pathology
11:15 Rita Sarkis (Switzerland) et al.
MarrowQuant 2.0: clinical application of a user-friendly digital hematopathology tool for human bone marrow trephine biopsies
11:25 Juan A. Retamero (United States) et al.
AI model trained to detect breast cancer metastasis to lymph node shows robust performance for micrometastases and isolated tumor cells.
11:35 Justinas Besusparis (Lithuania) et al.
Assessment of glomerular patterns of injury by machine learning methods
11:45 Linda Studer (Switzerland) et al.
Budding-T-cell score is a potential predictor for more aggressive treatment in pT1 colorectal cancers
11:55 Matteo Botteghi (Italy) et al.
CYTOFastUrine: An Innovative Integrated Solution For Automated Urine Cytology Diagnostics
12:05 Jessica Calvo (France) et al.
A Multi-Feature AI Solution for Diagnosis Support in Gastric Biopsies: A Prospective Multi-Site Clinical Study

Albrecht Stenzinger Germany

13:45 Frederick Klauschen Germany
AI in cancer research and molecular diagnostics
14:05 Maximilian Leitheiser (Germany) et al.
Machine Learning Models Predict the Primary Sites of Head and Neck Squamous Cell Carcinoma Metastases Based on DNA Methylation
14:15 John Connelly (United Kingdom) et al.
Using machine learning to infer whole genome duplication from tumour nuclear morphology
14:25 Nadine Flinner (Germany) et al.
Bagging ensemble cNN outperforms conventional laboratory staining methods in predicting molecular subtypes of gastric adenocarcinoma
14:35 Philipp Keyl (Germany) et al.
Patient-level proteomic network prediction by explainable artificial intelligence
14:45 Raquel Romero-Garcia (Spain) et al.
A deep learning approach in the prediction of gene mutations using hematoxylin-eosin images in breast cancer

Frederick Klauschen Germany
Peter Hufnagl Germany
Inti Zlobec Switzerland
David Horst Germany

Filippo Fraggetta Italy
Paul van Diest The Netherlands

16:00 Philipp Ströbel Germany
Implementation of a fully digital workflow: a critical review after two years
16:20 Catarina Eloy Portugal
Do the best for your patients - start the digital transformation of your lab!
16:40 Chris Gorman (United States) et al.
A standard-based computational image analysis workflow for scalable and interoperable AI model development and deployment
16:50 Paola Chiara Rizzo (Italy) et al.
Technical and Diagnostic Issues with Whole Slide Imaging in Validation Studies
17:00 Rutger H.J. Fick (France) et al.
An AI-supported solution to improve the digital pathology workflow for optimized breast cancer treatment decision-making
17:10 Alessandro Caputo (Italy) et al.
A combined molecular/digital approach to the cervical cancer screening program in Sicily (Italy): a preliminary report
17:20 Deniz Bayçelebi (Turkey) et al.
Implementation of Digital Pathology Workflow for Routine Primary Diagnosis in a Large Private Hospital Network

Norman Zerbe Germany
Filippo Fraggetta Italy

09:00 Peter Schirmacher Germany
Digitalisation and Pathology – ESP Aspects and Strategy
09:20 Inti Zlobec Switzerland
Tissue microarrays as tools in the digital era: useful or useless?

Norman Zerbe Germany
Sabine Leh Norway

09:45 Junya Fukuoka Japan
Implementation of Digital Pathology and AI model to pathology practice in Japan
10:05 Andrey Bychkov Japan
Discover Asia: a brief guide to digital pathology and AI on the continent

Gunter Haroske Germany
Mikael Wintell Sweden

11:00 Riki Merrick United States
IHE PaLM profiles for Digital Pathology: Enhancing interoperability of digital images across, electronic health record systems (EHR), laboratory information systems (LIS), imaging systems (IMS, PACS), and scanners
11:20 Markus Herrmann United States
Enabling quantitative tissue imaging and artificial intelligence in pathology through interoperable tools and services
11:40 Christoph Jansen (Germany) et al.
The EMPAIA approach: building bridges between existing AI solutions and digital pathology systems by providing open specifications
11:50 Michael Franz (Germany) et al.
Anonymization of Whole Slide Images for Research and Education

12:00 Gunter Haroske Germany
Current topics from IHE PaLM
12:15 Mikael Wintell Sweden
Current topics from DICOM WG-26

Peter Boor Germany

14:00 Paul van Diest The Netherlands
AI implementation in pathology practice: the UMC Utrecht roadmap
14:30 Emma Rewcastle (Norway) et al.
Prognostic evaluation of endometrial hyperplasia using an AI-based image analysis tool on whole slide images
14:40 Ruoyu Wang (United Kingdom) et al.
Deep Learning for HPV Infection Prediction in Head and Neck Cancers from H&E Whole Slide Images
14:50 Abhinav Sharma (Sweden) et al.
Evaluating the prognostic performance of a deep learning-based model that reproduces NHG histological grading in breast cancer
15:00 Miriam Angeloni (Germany) et al.
Can deep learning predict tumor heterogeneity in upper tract urothelial carcinoma?
15:10 Nidhi Bhatt (United Kingdom) et al.
Tumour Region Identification and Tumour Proportion Score Estimation of PD-L1 Expression in Non-Small Cell Lung Carcinoma Using Deep Learning
15:20 Kento Iida (Japan) et al.
Development of cytopathology support system using the homology concept

Kurt Zatloukal Austria
Christian Dierks Germany

16:00 Janos Hackenbeck Germany
The road to a CE-marked IVD and the particularities for AI-based medical device software
16:20 Christian Dierks Germany, Markus Gollrad Germany
Legal framework and things to watch in AI for pathology
16:35 Markus Plass (Austria) et al.
The role of explainable AI in regulatory practices

Norman Zerbe Germany
Vincenzo Della Mea Italy

16:45 Esther Abels United States
Regulatory considerations for Medical Device Software (MDSW) / Software as a Medical Device (SaMD)
17:05 Matthew Hanna United States
Current state of digital pathology and ML/AI in the US - the DPA perspective

Artyom Borbat Russia

09:00 Andrew Janowczyk United States
The quest for reproducible quality control in digital pathology
09:20 Xiaoyi Ji (Sweden) et al.
Physical Color Calibration of Digital Pathology Scanners for Deep Learning Based Diagnosis of Prostate Cancer
09:30 Tomé Albuquerque (Portugal) et al.
Quality checkpoint in pathology specimens handling: an AI system to automate fragment detection and count.
09:40 Vaughn Spurrier (United States) et al.
Automated Quality Control of Whole Slide Images Using Artificial Intelligence
09:50 Rasmus Kiehl (Germany) et al.
Automated detection of crush artefact in surgical pathology specimens using deep learning
10:00 Rob Sykes (United States) et al.
Building Clinical-Grade Artificial Intelligence Tools for Breast Cancer from the Ground Up
10:10 David Ameisen (France) et al.
Profiling images for better Quality Control in Digital Pathology
10:20 Artyom Borbat (Russia) et al.
Quality issues while setting up country wide digital pathology consultancy service in a limited resources

Rasmus Kiehl Germany
Andrew Janowczyk United States

11:00 Anirban Mukhopadhyay Germany
Federated Stain Normalization – when AI meets clinical reality
11:15 Tasneem Talawalla (United States) et al.
PatchSorter a high throughput open-source digital pathology tool for histologic object labeling
11:25 Rosalie Kletzander (Germany) et al.
Few-Label Adaptation using Multi-ProtoNets – an Experiment on Urothelial Carcinomas
11:35 Johannes Lotz (Germany) et al.
Comparison of Consecutive and Re-stained Sections for Virtual Multi-Staining by Image Registration

Vincenzo Della Mea Italy
Lewis Hassell United States

11:45 Rajendra Singh United States
Pathology education - anytime, anywhere on any device
12:10 Laurence Pesesse (Belgium) et al.
Alternating a MOOC with face-to-face sessions: a blended design to teach Histology
12:20 Artyom Borbat (Russia) et al.
A pilot study for postgraduate teaching pathology with virtual microscopy

Vincenzo L'Imperio Italy
Daniel Racoceanu France
14:00 Mark Eastwood (United Kingdom) et al.
Malignant Mesothelioma Subtyping of Tissue Images via Sampling Driven Multiple Instance Prediction
14:10 Philipp Jurmeister (Germany) et al.
DNA methylation-based classification of sinonasal tumors
14:20 James Mansfield (Denmark) et al.
Novel analysis method for in-situ spatial phenotyping of cell populations in multimarker imagery
14:30 Hilde J.G. Smits (The Netherlands) et al.
Validation of automated positive cell detection of immunohistochemically stained laryngeal tumor tissue using QuPath digital image analysis
14:40 Neda Azarmehr (United Kingdom) et al.
Application of neural architecture search technique in nuclear and epithelium segmentation in digital pathology images of oral dysplasia
14:50 Fan Fan (United States) et al.
CohortFinder: an open-source tool for quantitively partitioning datasets to improve deep learning model robustness
15:00 Stefan Reinhard (Switzerland) et al.
Ki-67 in breast cancer: do different algorithms and file formats lead to the same results?
15:10 Krzysztof Krawczyk (Sweden) et al.
Upconversion nanoparticles as labels for histopathological tissue evaluation
15:20 Mircea-Sebastian Serbanescu (Romania) et al.
Morphological analysis of nodular and micronodular basal cell carcinoma subtypes through texture analysis and semantic segmentation performance

Mircea-Sebastian Serbanescu Romania
David Ameisen France

16:00 Peter Hufnagl Germany
Resilience in AI and ML systems
16:30 Siddhesh Thakur (United States) et al.
Deep Learning Optimization for Whole Slide Image Analysis in Low-Resource Environments
16:40 Umair Akhtar Hasan Khan (Finland) et al.
Unsupervised Transfer Learning Boosts AI-based Virtual Staining in Histology
16:50 Sara P. Oliveira (Portugal) et al.
Computer-aided tool for CRC diagnosis: from the AI model to the clinical software prototype
17:00 Yuri Tolkach (Germany) et al.
Tumor detection and regression grading in esophageal adenocarcinomas
17:10 Romain Mormont (Belgium) et al.
Relieving pixel-wise labeling effort for pathology image segmentation by using self-training to learn from sparsely-annotated data
17:20 Nita Mulliqi (Sweden) et al.
A robust artificial intelligence approach for histopathological evaluation of prostate biopsies

Nasir Rajpoot United Kingdom
Viktor Kölzer Germany

09:00 Jens Rittscher United Kingdom
Linking traditional pathology and novel descriptors by machine learning
09:30 Adam J Shephard (United Kingdom) et al.
Fully Automated Attention based Multiple Instance Learning Predicts the Presence of Oral Epithelial Dysplasia in Whole Slide Images
09:40 Marie-Lisa Eich (Germany) et al.
Deep learning-based renal cell carcinoma detection and classification of common subtypes in histological sections
09:50 Marco Laurino (Italy) et al.
Tumor-infiltrating lymphocytes recognition in melanoma by open source deep learning convolutional neuronal network
10:00 Thomas Mrowiec (Germany) et al.
Characterization of tumor-microenvironment in H&E stained non-small cell lung cancer samples using immunohistochemistry-informed AI
10:10 Elias Baumann (Switzerland) et al.
Automatic quantification of “myxoid” desmoplastic stroma in colorectal cancer: a heterogeneous feature and challenging task
10:20 Srijay Deshpande (United Kingdom) et al.
Generation of Synthetic Colorectal Cancer Histology Images from Bespoke Glandular Layouts

Jeroen van der Laak The Netherlands
Thomas Wiegand Germany
Daniel Racoceanu France
Nasir Rajpoot United Kingdom
Frederick Klauschen Germany
Norman Zerbe Germany