The poster authors will be at their posters during the poster reception.
All information regarding the posters will be provided soon.
| P 01 |
Serbanescu Mircea-Sebastian (Romania) et al. Introducing ChatGPT to Image Classification for Histopathology |
| P 02 |
Zehra Talat (Pakistan) Dawn of AI enabled digital pathology in developing world- From benefits to challenges and possible solutions |
| P 03 |
Joona Pohjonen (Finland) et al. Augment like there’s no tomorrow: Consistently performing neural networks for medical imaging |
| P 04 |
Filomena Barreto (Portugal) et al. Harry Potter: a AI tool for diagnostic purposes |
| P 05 |
Ruoyu Shi (Singapore) et al. Three dimensional surface scanning of surgical specimens for potential incorporation into routine macroscopic examination workflow- a pilot study |
| P 06 |
Philippe Weitz (Sweden) et al. The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue |
| P 09 |
Nicolas Nerrienet (France) et al. End-to-end pipeline for automatic grading of IHC biomarkers |
| P 10 |
Nora Manstein (Germany) et al. Integrating AI into pathologists' work life |
| P 11 |
Meredith Lodge (United States) et al. Halo Breast AI: a Deep Learning Workflow for Clinical Scoring of HER2, ER, PR & Ki67 Immunohistochemistry (IHC) in Breast Cancer Tissue |
| P 12 |
Petr Kuritcyn (Germany) et al. Uncertainty calibrated deep tissue classification in histopathology |
| P 13 |
Rita Sarkis (Switzerland) et al. Evaluation of Bone Marrow Stromal Edema by quantitative digital pathology |
| P 14 |
Kathy Robinson (Australia) et al. A longitudinal Australasian review of consultant driven synchronous large group pathology education using digital slides |
| P 15 |
Nazish Jaffar (Pakistan) et al. Ki 67 Quantification by Digital Image-Based AI Software & Its Correlation with Eye Ball Method in Breast Cancer |
| P 16 |
Christian Gebbe (Germany) et al. Uncertainty-guided iterative training of supervised deep learning algorithms for digital pathology |
| P 17 |
Hussein Naji (Germany) et al. Automated Nuclei Segmentation of H&E and IHC stained Whole Slide Images of Diffuse Large B-Cell Lymphoma |
| P 18 |
Moritz Fuchs (Germany) et al. Improving the Reliability of Deep Learning in Computational Pathology |
| P 19 |
Yiyu Hong (South Korea) et al. Virtual cytokeratin and LCA staining of gastric carcinomas to classify tumor microenvironment |
| P 20 |
Maren Høibø (Norway) et al. Segmentation of epithelial cells in hematoxylin and eosin-stained histopathological breast cancer slides |
| P 21 |
Vincenzo Della Mea (Italy) et al. Teaching Digital Pathology to future laboratory technicians: the Udine experience |
| P 22 |
Adam Shephard (United Kingdom) et al. A Fully Automated Pipeline for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia |
| P 23 |
Mai Bui (Germany) et al. Few-shot learning of domain-invariant networks for domain-agnostic nuclei instance segmentation |
| P 24 |
Leslie Solorzano (Sweden) et al. Ensembles for improved detection of invasive breast cancer in histological images |
| P 25 |
Yanbo Feng (Sweden) et al. Exploring CNN activation patterns associated with the size of cancerous area in histopathology image and its relationship with model feature maps |
| P 26 |
Srijay Deshpande (United Kingdom) et al. Interactive Synthesis of Histology Images from Bespoke Cellular Layouts |
| P 27 |
Michaela Benz (Germany) et al. DECISION-MAKING SUPPORT SYSTEM FOR DIAGNOSIS OF ONCOPATHOLOGIES |
| P 28 |
Melanie Lubrano (France) et al. Deep Learning Model for Grading Head and Neck Squamous Lesions with a Grade-Sensitive Confidence Measure |
| P 29 |
Swapnil Rane (India) et al. Cancer Imaging Biobank(CAIB)-AI ready health data from India |
| P 30 |
Johanna Palacios Ball (Spain) et al. ¿Is DICOM really important? What we have learned during our digital transformation process |
| P 31 |
Oded Ben-David (Israel) et al. Virtual stain-multiplexing CD68 for PD-L1 IHC 22C3 pharmDx scanned NSCLC tissue slides |
| P 32 |
Bhakti Baheti (United States) et al. Interpretable whole slide image prognostic stratification of glioblastoma patients furthering current clinical knowledge |
| P 33 |
Filippo Ugolini (Italy) et al. Tumor infiltrating lymphocytes recognition in primary melanoma by deep learning convolutional neuronal network |
| P 34 |
Margaret Horton (United States) et al. The reading paradigm: How the sequence and presentation of AI results to pathologists influences endpoints and outcomes |
| P 35 |
Pedro C. Neto (Portugal) et al. To err or to say “I don't know"? A study on the usage of efficient mixed supervision with a rejection option to diagnose colorectal lesions on WSI |
| P 36 |
Masi Valkonen (Finland) et al. ACROBAT 2023: Analysis of multi-stain WSI registration algorithms under domain shift |
| P 37 |
Krzysztof Krawczyk (Sweden) et al. Upconversion nanoparticles as labels for histopathological tissue evaluation |
| P 38 |
Hammam M. AlGhamdi (United Kingdom) et al. Towards pan-cancer histology image classification with knowledge distillation |
| P 39 |
Wan Siti Halimatul Munirah Wan Ahmad (Malaysia) et al. Classification of Nasopharyngeal Cases using DenseNet Deep Learning Architecture |
| P 40 |
Wan Siti Halimatul Munirah Wan Ahmad (Malaysia) et al. Whole Slide Image scoring using DenseNet for ER-IHC: in search of optimal configuration |
| P 41 |
German Sergei (Germany) Characterization of chronic kidney diseases with Self-Supervised Learning techniques |
| P 42 |
Viktoryia Zakharava (Belarus) et al. Triple-negative breast cancer: structure and morphological features of immunohistochemical subtypes |
| P 43 |
Nicolò Caldonazzi (Italy) et al. Automatic detection of Lymph node metastasis: twenty years of evolution |
| P 44 |
Manahil Raza (United Kingdom) et al. Is Stain Augmentation All You Need for Domain Generalization? |
| P 45 |
Nur Basak Ozer (The Netherlands) et al. Intraoperative Cytological Diagnosis of Brain Tumors: A Preliminary Study Using Deep Learning Model |
| P 46 |
Elias Baumann (Switzerland) et al. Mapping the tumor microenvironment: Deep learning-based quantification of eosinophils and lymphocytes for patient outcome prediction in colon cancer |
| P 47 |
Nazanin Mola (Norway) Effect of stain normalization on estimation of kidney fibrosis with image analysis |
| P 48 |
Made Satria Wibawa (United Kingdom) et al. Digital Markers of Tumour Infiltrating Lymphocytes Predict Locoregional Recurrence-Free Survival in Nasopharyngeal Carcinoma |
| P 49 |
Thomas R Leech (United Kingdom) et al. PathLAKE Portal: A Hybrid Platform for Showcasing and Sharing PathLAKE Whole-Slide Images |
| P 50 |
Aleksandra Asaturova (Russia) et al. Morphologic criteria and CD138-positive cells counting for chronic endometritis: manual versus AI-based algorithms |
| P 51 |
Emre Karakok (Turkey) et al. A retrospective evaluation of artificial intelligence solution for prostate biopsies |
| P 52 |
Hatem A. Rashwan (Spain) et al. The BosomShield project: an integrative approach to diagnosis and prognosis of breast cancer relapse based on radiologic / pathologic image biomarkers |
| P 53 |
Mario Parreno-Centeno (United Kingdom) et al. A deep-learning framework to dissect histological age patterns of the breast tissue |
| P 54 |
Viktoryia Zakharava (Belarus) et al. Expression of metalloproteinases in assessing the effectiveness of therapy in patients with aggressive periodontitis |
| P 55 |
Till Nicke (Germany) et al. Multitask pretraining outperforms ImageNet in learning general representations in computational pathology |
| P 56 |
Philippe Weitz (Sweden) et al. Stratipath Breast: Deep Learning-Based Risk Stratification of Intermediate Risk Breast Cancers |
| P 57 |
Laura Mairinoja (Finland) et al. Quantifying micro- and macrovesicular steatosis in preclinical mouse models of NAFLD by a deep learning based image analysis of whole slide images |
| P 58 |
Chuer Zhang (United Kingdom) et al. Multicentre and Prospective: Multiplying the complexity to evaluate the health economics of AI for prostate cancer |
| P 59 |
Iancu Emil Plesea (Romania) et al. Age related remodeling of aortic diameter |
| P 60 |
Iancu Emil Plesea (Romania) et al. Aortic diameter remodeling depending on patient’s cause of death |
| P 61 |
Julius Drachneris (Lithuania) et al. Prediction of NMIPUC Relapse from Hematoxylin-Eosin Images using Deep Multiple Instance Learning in patients treated with BCG immunotherapy |
| P 62 |
Walter de Back (Germany) et al. Improving the efficiency and robustness of phenotyping in multiplex immunofluorescence whole slide imaging |
| P 63 |
Kesi Xu (United Kingdom) et al. Auto-NuClick: A dual-stage neural network for nuclear instance segmentation |
| P 64 |
Matteo Pozzi (Italy) et al. Diffusion models for WSI generation: a synthetic step towards supporting sharing and mitigating imbalance |
| P 65 |
Rita Canas-Marques (Portugal) et al. Fully Automated Artificial Intelligence Solution for Accurate HER2 IHC Scoring in Breast Cancer: Multi-Reader Study |
| P 66 |
Kajsa Ledesma Eriksson (Sweden) et al. Semantic Segmentation of DCIS in Breast Cancer Histopathology Whole Slide Images with Deep Learning |
| P 67 |
Lia DePaula Oliveira (United States) et al. Assessing Risk of Prostate Cancer Metastasis by Deep Learning in Surgically-Treated Patients |
| P 68 |
Eric Erak (United States) et al. Deep Learning-Based Identification of Lymph Node Metastasis in Prostate Cancer |
| P 69 |
DR JAYA JAIN (India) et al. Z stacking for WSI generation improves the TIL detections algorithm performance in Breast cancer cases |
| P 70 |
David Snead (United Kingdom) et al. Multi-site validation of digital pathology for the routine reporting of histopathology samples |
| P 71 |
Christian Mate (Germany) et al. On robustness and domain generalization of classification systems for leukocytes in peripheral Blood and Bone Marrow |
| P 72 |
Celine Degaillier (Belgium) et al. Digital pathology - from clinic to research. The UZBrussel - VUB experience |
| P 73 |
E Kontsek (Hungary) et al. Multivariate modelling of mid-infrared spectra of colorectal cancer |
| P 74 |
János Báskay (Hungary) et al. Reconstructing 3D histological structures using machine learning (AI) algorithms |
| P 75 |
E Kontsek (Hungary) et al. Colorectal cancer screening aided by AI |