The poster authors will be at their posters during the poster reception.
All information regarding the posters will be provided soon.
| 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 |
Gerardo Cazzato (Italy) et al. Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology |
| P05 |
Christian Harder (Germany ) et al. Enhancing Prostate Cancer Diagnosis: AI-Driven Virtual Biopsy for Optimal Targeted Biopsy Approach and Gleason Grading Strategy |
| P06 |
Tien-Jen Liu (United States) et al. A Comparative Study of Artificial Intelligence Integration in Bladder Cancer Screening: Enhancing Accuracy and Efficiency |
| P07 |
Rita Carvalho (Berlin, Germany) et al. AI-based stroma-tumor ratio quantification algorithm: evaluation of prognostic role in primary colorectal cancer |
| P08 |
Aleksandar Vodovnik (Norway) et al. Non-diagnostic time in digital pathology: An empirical study over ten-year period |
| P09 |
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 |
| P10 |
Jackson Jacobs (United States) et al. Standardizing Reporting of Core Needle Biopsy Tortuosity: BiopTort - A Computer Aided Protocol for Grading Tortuosity in Clinical Workflows |
| P11 |
Jennifer Andreasson (USA) et al. Prospective Technical Deployment of Artificial Intelligence Pathology Platform to Three UK Hospitals for Real-time Use in Patient Pathways |
| P12 |
Lorenzo Nibid (Italy) et al. Deep pathomics in locally advanced Non-Small Cell Lung Cancer: a digital tool for predicting response to chemoradiotherapy |
| P13 |
Susanne E. Pors (Denmark) et al. Automated AI-assisted assessment of NAS and fibrosis stage in biopsy-confirmed rodent models of MASH |
| P14 |
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 |
| P15 |
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 |
| P16 |
Marika Viatore (Italy) et al. Digital pathology and AI-based approaches characterizing the interactions between tumor microenvironment components and their spatial distribution |
| P17 |
Glenn Broeckx (Belgium) et al. Multi-Reader Study of a Fully Automated Artificial Intelligence Solution for HER2 Immunohistochemistry Scoring in Breast Cancer |
| P18 |
Pedro Montero Pavón (Spain) et al. Machine learning algorithm for the detection of Reed-Sternberg cells from classical Hodgkin Lymphoma. |
| P19 |
Clara Simmat (France) et al. Deep Learning Enabled End-to-end Pipeline for Automatic Grading of HER2 in Breast Cancer |
| P20 |
Jelica Vasiljević (Basel, Switzerland) et al. ActiveVisium: An AI-Based Assistant for Rapid Visium Spot Annotation |
| P21 |
Wei-Lei Yang (United States) et al. Developing an Artificial Intelligence-based Logistic Model for Automated Bladder Cancer Screening in Digital Urine Cytology |
| P22 |
Rudolf Herdt (Germany) et al. Explaining a Deep U-Net Trained for Tumor Detection in Histological Whole Slide Images |
| P23 |
Melissa Linkert (United States) et al. The next generation of open standards for scalable Digital Pathology visualization and analysis |
| P24 |
David Joon Ho (Korea) A Multi-Institutional Tissue Segmentation Model for Breast Histopathology Images |
| P25 |
Volodymyr Chapman (United Kingdom) et al. Histopathology classifier for high risk Diffuse Large B-Cell Lymphoma |
| P26 |
Marika Karjalainen (Finland) et al. Aiforia Clinical Suite for Prostate Cancer: A Holistic Assistive Tool for Prostate Cancer Management |
| P27 |
Romi Feddersen (Germany) et al. Spatscores – a novel user friendly application for high throughput analysis of spatial relationships in the tumor microenvironment |
| P28 |
Isil Yildiz-Aktas (USA) Digital pathology and artificial intelligence in developing countries: a scoping review |
| P29 |
Dr Anila Sharma (India) et al. Application of lean methodology to frozen section workflow - An audit of present practices at a single large oncology center |
| P30 |
Dr Anila Sharma (India) Turn around time as a metric of quality control in surgical pathology departmentv |
| P31 |
Filip Winzell (Sweden) et al. Attention-based Multiple Instance Learning to Estimate Risk of Prostate Cancer from Whole Slide Images |
| P32 |
Anna Välimäki (Finland) et al. Multicenter digital pathology user experience survey of 54 Finnish pathologists |
| P33 |
Michel Petrovic (Switzerland) Digital Pathology end-to-end workflow - Conceptual landscape at Roche pRED |
| P34 |
Rosen Dimitrov (Bulgaria) et al. Advancing Sustainability in Digital Pathology and AI through Hybrid Data Infrastructure Implementation with Tiger BRIDGE |
| P35 |
Chan Kwon Jung (Republic of Korea) et al. Deep learning models for predicting lymph node metastasis in thyroid cancer core needle biopsy samples |
| P36 |
Karolina Punovuori (Finland) et al. Cancer cell state determines tumor-stroma interaction dynamics and patient survival in head and neck cancer |
| P37 |
Abhinav Sharma (Sweden) et al. Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images |
| P38 |
Azar Kazemi (Munich, Germany) et al. Machine Learning-Based Tumor-Infiltrating Lymphocytes Analysis in Colorectal Cancer: Techniques, Performance Metrics, and Clinical Outcomes |
| P39 |
Shew Fung Wong (Malaysia) et al. Spatial Transcriptomic Analysis of Colorectal Cancer: Identification Of Biomarkers Linked To Muscle Invasion |
| P40 |
Iancu Emil Plesea (Romania) et al. Morphometric analysis of aortic diameter and wall layers depending on sex |
| P41 |
Maximilian C. Koeller (Austria) et al. Virtual Microscopy for Academic Pathology Education at the Medical University of Vienna |
| P42 |
Giulia L. Baroni (Italy) et al. Vision Transformers for Breast Cancer Classification |
| P43 |
Melvin Geubbelmans (Belgium) et al. Investigating the effect of resolution differences on segmentation using Whole Slide Images |
| P44 |
Sandrina Martens (Diepenbeek, Belgium) et al. An educational annotation platform to improve knowledge retention for histopathology |
| P45 |
Jaya Jain (India) et al. Novel volumetric scanning method with dynamic Z stacks yields high quality PAP smears at par with traditional microscope |
| P46 |
Alessio Fiorin (Spain) et al. A robust region of interest registration approach on breast histological whole slide images |
| P47 |
Daniela Rodrigues (Estados Unidos) et al. Automated detection of out-of-focus regions by SlideQC BF on the FocusPath dataset |
| P48 |
Jaya Jain (India) et al. Integration of slide quality parameters with WSI DICOM expands the outreach of digital pathology |
| P49 |
Mario Parreno-Centeno (UK) et al. Integrated deep learning and graph-based exploration of cellular dormancy in histopathology images of colon cancer |
| P50 |
Ilknur Turkmen (Türkiye) et al. What Pathologists want from AI ( if had an opportunity of Alaaddin’s magic lamp)? |
| P51 |
Ilknur Turkmen (Türkiye) et al. Informatics in Pathology :A Preliminary Study About the Pathologists from Türkiye |
| P52 |
Serdar Balci (Türkiye) et al. Automatic H.pylori detection on Warthin-Starry WSI with AI |
| P53 |
Pelin Balci (Türkiye) et al. Extracting information from semi-structured Pathology Reports |
| P54 |
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 |
| P55 |
Gulfize Coskun (Türkiye) et al. Evaluation of Ki-67 in WSI subjected to compression |
| P56 |
Laura Valeria Perez-Herrera (Spain) et al. Deep Learning-Based Classification of SOX2 Expression Levels in Breast Cancer Whole Slide Images |
| P57 |
Dominique Van Midden (The Netherlands) et al. Introducing the MONKEY Challenge: Machine-learning for Optimal detection of iNflammatory cells in the KidnEY |
| P58 |
Quoc Dang Vu (United Kingdom) et al. Streamlining Colon Biopsy Screening with Interpretable Machine Learning |
| P59 |
Navid Alemi (United Kingdom) et al. Towards Comprehensive Segmentation of Colorectal Histology Images: A Multi-Task Learning Approach |
| P60 |
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 |
| P61 |
Nicolas Nerrienet (France) et al. Deep Learning Solutions for Quality Control in Histopathology |
| P62 |
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 |
| P63 |
Radwan Etwebi (Norway) et al. Calculation of Ki-67-index in gastroenteropancreatic neuroendocrine tumors with open-source software QuPath |
| P64 |
Kim Nijsten (Belgium ) et al. Hidden Treasures: Combining (cyclic) multiplex immunofluorescence with standard HE staining for whole slide imaging |
| P65 |
Greta Markert (United Kingdom) et al. Tissue Detection and Segmentation in Diverse Histopathology Images |
| P66 |
Leon Gugel (Israel) et al. A new deep learning model that accurately predicts exome-wide gene expression from histopathology slides |
| P67 |
Martynas Riauka (Lithuania) et al. Artificial Intelligence-enabled Nonlinear Multimodal Polarimetric Microscopy for Melanoma Diagnostics and Prognostics |
| P68 |
David Anglada-Rotger (Espanya) et al. From Self-Supervised Feature Extraction to Diagnosis: A ViT MIL Approach for Gastric Adenocarcinoma |
| P69 |
David Anglada-Rotger (Espanya) et al. Semantic segmentation combined with nuclei center detection for quantification of KI-67 histopathological images |