The poster authors will be at their posters during the poster reception.
All information regarding the posters will be provided soon.

P01 Zehra Talat (Pakistan)
Use of a novel deep learning model for detection of LD bodies and granulomas in cases of cutaneous leishmaniasis- A neglected tropical disease of the developing world
P02 Martin Kristensson (Denmark) et al.
Quantifying Enhanced Sensitivity in HER2-Low Spectrum: Comparing HercepTest and 4B5 Utilizing the Visiopharm HER2 APP
P03 Misaki WAYENGERA (Uganda) et al.
Establishing a digital pathology repository for h & e stained slides & ffpes stored at the pathology department since 1942
P04 Christian Harder (Germany ) et al.
Enhancing Prostate Cancer Diagnosis: AI-Driven Virtual Biopsy for Optimal Targeted Biopsy Approach and Gleason Grading Strategy
P05 Tien-Jen Liu (United States) et al.
A Comparative Study of Artificial Intelligence Integration in Bladder Cancer Screening: Enhancing Accuracy and Efficiency
P06 Rita Carvalho (Berlin, Germany) et al.
AI-based stroma-tumor ratio quantification algorithm: evaluation of prognostic role in primary colorectal cancer
P07 Aleksandar Vodovnik (Norway) et al.
Non-diagnostic time in digital pathology: An empirical study over ten-year period
P08 Zhuoyan Shen (United Kingdom) et al.
A Deep Learning Framework Deploying ‘Segment Anything’ to Detect Mitotic Figures from Haematoxylin and Eosin-Stained Slides for Multiple Tumour Types
P09 Jackson Jacobs (United States) et al.
Standardizing Reporting of Core Needle Biopsy Tortuosity: BiopTort - A Computer Aided Protocol for Grading Tortuosity in Clinical Workflows
P10 Jennifer Andreasson (USA) et al.
Prospective Technical Deployment of Artificial Intelligence Pathology Platform to Three UK Hospitals for Real-time Use in Patient Pathways
P11 Lorenzo Nibid (Italy) et al.
Deep pathomics in locally advanced Non-Small Cell Lung Cancer: a digital tool for predicting response to chemoradiotherapy
P12 Susanne E. Pors (Denmark) et al.
Automated AI-assisted assessment of NAS and fibrosis stage in biopsy-confirmed rodent models of MASH 
P13 Rebecca Polidori (Milan, Italy) et al.
AI-powered analysis of tissue slides to reveal the cellular composition and the spatial organization of the tumor microenvironment
P14 Randall Woltjer (United States) et al.
White matter hyperintensities and the surrounding normal appearing white matter are associated with water channel disruption in the aged human brain
P15 Marika Viatore (Italy) et al.
Digital pathology and AI-based approaches characterizing the interactions between tumor microenvironment components and their spatial distribution
P16 Glenn Broeckx (Belgium) et al.
Multi-Reader Study of a Fully Automated Artificial Intelligence Solution for HER2 Immunohistochemistry Scoring in Breast Cancer
P17 Pedro Montero Pavón (Spain) et al.
Machine learning algorithm for the detection of Reed-Sternberg cells from classical Hodgkin Lymphoma.
P18 Clara Simmat (France) et al.
Deep Learning Enabled End-to-end Pipeline for Automatic Grading of HER2 in Breast Cancer
P19 Jelica Vasiljević (Basel, Switzerland) et al.
ActiveVisium: An AI-Based Assistant for Rapid Visium Spot Annotation
P20 Wei-Lei Yang (United States) et al.
Developing an Artificial Intelligence-based Logistic Model for Automated Bladder Cancer Screening in Digital Urine Cytology
P21 Rudolf Herdt (Germany) et al.
Explaining a Deep U-Net Trained for Tumor Detection in Histological Whole Slide Images
P22 Melissa Linkert (United States) et al.
The next generation of open standards for scalable Digital Pathology visualization and analysis
P23 Volodymyr Chapman (United Kingdom) et al.
Histopathology classifier for high risk Diffuse Large B-Cell Lymphoma
P24 Marika Karjalainen (Finland) et al.
Aiforia Clinical Suite for Prostate Cancer: A Holistic Assistive Tool for Prostate Cancer Management
P25 Isil Yildiz-Aktas (USA)
Digital pathology and artificial intelligence in developing countries: a scoping review
P26 Anila Sharma (India)
An Institutional Experience Analyzing Amendments to Cut Error Rates in Surgical Pathology
P27 Dr Anila Sharma (India)
Turn around time as a metric of quality control in surgical pathology departmentv
P28 Filip Winzell (Sweden) et al.
Attention-based Multiple Instance Learning to Estimate Risk of Prostate Cancer from Whole Slide Images
P29 Anna Välimäki (Finland) et al.
Multicenter digital pathology user experience survey of 54 Finnish pathologists
P30 Chan Kwon Jung (Republic of Korea) et al.
Deep learning models for predicting lymph node metastasis in thyroid cancer core needle biopsy samples
P31 Karolina Punovuori (Finland) et al.
Cancer cell state determines tumor-stroma interaction dynamics and patient survival in head and neck cancer
P32 Abhinav Sharma (Sweden) et al.
Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images
P33 Azar Kazemi (Munich, Germany) et al.
Machine Learning-Based Tumor-Infiltrating Lymphocytes Analysis in Colorectal Cancer: Techniques, Performance Metrics, and Clinical Outcomes
P34 Shew Fung Wong (Malaysia) et al.
Spatial Transcriptomic Analysis of Colorectal Cancer: Identification Of Biomarkers Linked To Muscle Invasion
P35 Iancu Emil Plesea (Romania) et al.
Morphometric analysis of aortic diameter and wall layers depending on sex
P36 Maximilian C. Koeller (Austria) et al.
Virtual Microscopy for Academic Pathology Education at the Medical University of Vienna
P37 Giulia L. Baroni (Italy) et al.
Vision Transformers for Breast Cancer Classification
P38 Melvin Geubbelmans (Belgium) et al.
Investigating the effect of resolution differences on segmentation using Whole Slide Images
P39 Sandrina Martens (Diepenbeek, Belgium) et al.
An educational annotation platform to improve knowledge retention for histopathology
P40 Jaya Jain (India) et al.
Novel volumetric scanning method with dynamic Z stacks yields high quality PAP smears at par with traditional microscope
P41 Alessio Fiorin (Spain) et al.
A robust region of interest registration approach on breast histological whole slide images
P42 Daniela Rodrigues (Estados Unidos) et al.
Automated detection of out-of-focus regions by SlideQC BF on the FocusPath dataset
P43 Jaya Jain (India) et al.
Integration of slide quality parameters with WSI DICOM expands the outreach of digital pathology
P44 Mario Parreno-Centeno (UK) et al.
Integrated deep learning and graph-based exploration of cellular dormancy in histopathology images of colon cancer
P45 Ilknur Turkmen (Türkiye) et al.
Informatics in Pathology :A Preliminary Study About the Pathologists from Türkiye
P46 Serdar Balci (Türkiye) et al.
Automatic H.pylori detection on Warthin-Starry WSI with AI
P47 Pelin Balci (Türkiye) et al.
Extracting information from semi-structured Pathology Reports
P48 Jari Claes (Belgium) et al.
Detecting tumor regions in lung cancer tissue sections using Gaussian Process modelling of cell-specific features generated by artificial intelligence
P49 Gulfize Coskun (Türkiye) et al.
Evaluation of Ki-67 in WSI subjected to compression
P50 Laura Valeria Perez-Herrera (Spain) et al.
Deep Learning-Based Classification of SOX2 Expression Levels in Breast Cancer Whole Slide Images
P51 Dominique Van Midden (The Netherlands) et al.
Introducing the MONKEY Challenge: Machine-learning for Optimal detection of iNflammatory cells in the KidnEY
P52 Quoc Dang Vu (United Kingdom) et al.
Streamlining Colon Biopsy Screening with Interpretable Machine Learning
P53 Navid Alemi (United Kingdom) et al.
Towards Comprehensive Segmentation of Colorectal Histology Images: A Multi-Task Learning Approach
P54 Maité CHAMOURIN (FRANCE) et al.
Leveraging AI-powered tools for a streamlined analysis of multiplex H&E and IHC images to characterize the tumor landscape
P55 Nicolas Nerrienet (France) et al.
Deep Learning Solutions for Quality Control in Histopathology
P56 Bojing Liu (Sweden) et al.
Differences in response to adjuvant chemotherapy between breast cancer risk groups defined by a prognostic AI-based risk stratification model
P57 Radwan Etwebi (Norway) et al.
Calculation of Ki-67-index in gastroenteropancreatic neuroendocrine tumors with open-source software QuPath
P58 Greta Markert (United Kingdom) et al.
Tissue Detection and Segmentation in Diverse Histopathology Images
P59 Martynas Riauka (Lithuania) et al.
Artificial Intelligence-enabled Nonlinear Multimodal Polarimetric Microscopy for Melanoma Diagnostics and Prognostics
P60 David Anglada-Rotger (Espanya) et al.
Semantic segmentation combined with nuclei center detection for quantification of KI-67 histopathological images