Posters will be on display during the entire Congress in the Industry Exhibition (Ballroom III) from June 16th-18th. There will be a dedicated Poster Session & Get-Together on Thursday, June 16th at 6:30pm, in the Exhibition hall.

P01 Peter Ouillette (United States) et al.
Virtual multidisciplinary tumor board impact after the COVID-19 pandemic
P02 Zichen Zhang (China) et al.
Automated Scoring System of HER2 in Pathological Images under the Microscope
P03 Huu-Giao Nguyen (Switzerland) et al.
Automatic lymphocyte quantification in virtual CD20-CD3 staining generated from H&E images using GAN colorization
P04 Francesco Martino (Austria) et al.
A Pix2Pix model for Ki-67 tissue expression prediction on H&E-stained OSCC histopathological images
P05 Philippe Weitz (Sweden) et al.
Prediction of Ki67 scores from H&E stained breast cancer sections using convolutional neural networks
P06 Thien Do (Switzerland) et al.
Cell segmentation and quantification on H&E images using vision transformer model
P07 Nicole Bussola (Italy) et al.
Quantification of the Immune Content in Neuroblastoma: Deep Learning and Topological Data Analysis in Digital Pathology
P08 Ana Frei (Switzerland) et al.
Improving cell classification with hard negative mining: an example of lymphocyte classification in colorectal cancer
P09 Anuradha Kar (France) et al.
A deep learning framework for stratification of Alzheimer’s disease patients using whole slide histopathological brain tissue images
P10 Lars Ole Schwen (Germany) et al.
Evaluating generic AutoML tools for computational pathology
P11 Talat Zehra (Pakistan)
Adoption of digital techniques and use of artificial intelligence in Histopathology - A validation study on Chorionic Villi
P12 Andrea Pennisi (Belgium) et al.
Oral Squamous Cell Carcinoma Image Segmentation Using a Multi-encoder U-Net
P13 Daniel García León (Sweden) et al.
Fine-grained two-step segmentation approach to process digital pathology images in breast cancer
P14 Robin Sebastian Mayer (Germany) et al.
Retaining Whole Slide Image Information for Cancer Prediction
P15 Gerardo Cazzato (Italia) et al.
Will an AI algorithm ever replace the dermatopathologist? A single institutional study
P16 Patricia Raciti (United States) et al.
AI-assisted detection of Perineural Invasion by Multiple Instance Learning shows robust diagnostic output
P17 Satomi Hatta (Japan) et al.
Improvement of detection accuracy of deep learning in thyroid cancer cells using several histological resources
P18 Judith Judith Sandbank (Israel) et al.
Implementation of an AI Solution for Primary Breast Cancer Diagnosis and Reporting in Clinical Routine
P19 Christian Matek (Germany)
Cytomorphologic evaluation of bone marrow sigle-cell images using deep learning methods
P20 Kimmo Kartasalo (Sweden) et al.
Artificial Intelligence for Detecting Perineural Invasion in Prostate Biopsies
P21 Juan A. Retamero (United States) et al.
But will AI work on my patients? Generalizability is critical for the clinical use of AI in prostatic biopsy diagnosis and opens its use in screening.
P22 Amjad Khan (Switzerland) et al.
Accurate colorectal cancer lymph node metastasis detection using ensemble models trained on breast sentinel nodes
P23 Maria Orsaria (Italy) et al.
Image analysis of diaphragm muscle cells in Covid-19 patients
P24 Tomasz Religa (Poland) et al.
Image analysis for cervical cancer screening using deep learning
P25 Pedro Oliveira (United Kingdom) et al.
Deep learning for sub-classification of Gleason pattern 4 in prostate cancer
P26 Juan A. Retamero (United States) et al.
AI in routine prostatic biopsy diagnosis leads to improved diagnostic accuracy and efficiency gains
P27 Jillian Sue (United States) et al.
Pathologist-driven experience dictates design of AI-based digital diagnostics
P28 Talat Zehra (Pakistan)
Use of Artificial Intelligende in diagnosing malaria - An endemic disease of developing countries
P29 Mohammad Faizal Ahmad Fauzi (Malaysia) et al.
Computer-Aided System for Hormone Receptor Expression in Breast Carcinoma
P30 Clara Simmat (France) et al.
Performance study of CLEO Mitosis, an automatic mitosis detection tool for invasive breast cancer
P31 Leslie Solorzano (Sweden) et al.
Classification of DCIS and Invasive cancer in Breast Cancer slides
P32 Vincenzo D'Angelo (Italy) et al.
Development of an AI-based tool for classification of OSCC histopathological images.
P33 Christian Abbet (Switzerland) et al.
Self-Rule to Multi Adapt automates the tumor-stroma assessment in colorectal cancer
P34 Rohit Thanki (UAE)
Design and Development of Intelligent Cancer Screening System for Highly Occurred Cancers among People of Europe
P35 Hajar El Agouri (Morocco) et al.
Application of Deep Learning in the histopathological diagnosis of breast cancer as a first Moroccan experience on a private dataset
P36 Markus Plass (Austria) et al.
Standardized phenotypic description of datasets of histological sections
P37 Suze Julia Roostee (Sweden) et al.
Unsupervised quantification of IHC stains in triple-negative breast cancer
P38 Dr Jaya Jain (India) et al.
Image Acquisition Algorithms to Enable Archival of Old Slides with Artefacts
P39 Justin E. Swartz (The Netherlands) et al.
Correlation and colocalization of HIF-1a and pimonidazole staining for hypoxia in laryngeal carcinomas: a digital, single-cell-based analysis
P40 Gabriela Izabela Baltatescu (Romania) et al.
Digital quantification of proliferation rate of the invasive breast carcinoma - inter-observer agreement and clinical validation
P41 Richard Salmon (United Kingdom) et al.
Quantitative Colour: Metric-Based QA for WSI Colour and Impact of Standardisation on Digital Pathology and AI
P42 Pietro Antonini (Italy) et al.
Rating Whole Slide Imaging Validation Studies in Cytology according to College of American Pathologists Guidelines
P43 Taryme Lopez Diaz (United States) et al.
Validation of Artificial Intelligence-Based System for Prostate Cancer Detection and Grading
P44 Rasmus Kiehl (Germany) et al.
EMPAIA Academy - a free advanced training program of the EMPAIA project
P45 Hilde J.G. Smits (The Netherlands) et al.
Validation of automated positive region detection of immunohistochemically stained laryngeal tumor tissue using QuPath digital image analysis.