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. |