9:00 | Arvydas Laurinavicius Lithuania Welcome words from the Congress Presidents |
9:05 | Norman Zerbe Germany Welcome words from ESDIP President |
9:10 | Olegas Niaksu Lithuania Digital Transformation of Healthcare – a national perspective from the Ministry of Health, Lithuania |
CHAIR
Arvydas Laurinavicius Lithuania
INVITED SPEAKER
Arvydas Laurinavicius Lithuania
INVITED SPEAKER
9:20 | Richard Levenson United States of America |
CHAIRS
Inti Zlobec Switzerland
Manuel Salto-Tellez Ireland
INVITED SPEAKER
Inti Zlobec Switzerland
Manuel Salto-Tellez Ireland
INVITED SPEAKER
9:50 | Hannah Williams Switzerland Resolving the transcriptional landscape of colorectal cancer through spatial transcriptomics |
ABSTRACTS
10:10 |
Spyridon Bakas United States of America
Detecting Histologic & Clinical Glioblastoma Patterns of Prognostic Relevance |
10:20 |
Elaine Chan Malaysia
Spatially Resolved Transciptomic Profilling Of Formalin-Fixed Colorectal Cancer (CRC) Tissues Between Well & Poorly Differentiated Tissue Areas |
10:30 |
Leander Van Eekelen The Netherlands
Immunotherapy response prediction for non-small cell lung cancer is improved by using cell-graphs of the tumor microenvironment |
CHAIR
Kurt Zatloukal Austria
INVITED SPEAKER
Kurt Zatloukal Austria
INVITED SPEAKER
11:15 | Naoko Tsuyama Japan Bridging the gap: enhancing pathologists’ interpretability of AI via feature visualisation |
ABSTRACTS
11:35 |
Bart Sturm The Netherlands
Deep learning predicts the effect of neo-adjuvant chemotherapy for patients with triple negative breast cancer |
11:45 |
Elaine Chan Malaysia
Pancreatic Cancer Classification Using Deep Convolutional Neural Network to Aid The Analysis of Fine Needle Aspirates for Pancreatic Cancer Diagnosis |
11:55 |
Anthony Manet Norway
Predicting prostate cancer outcome from histopathology section images using deep learning |
12:05 |
Waleed Ahmad Germany
Computational pathology platform for lung cancer: development and validation of diagnostic and prognostic algorithms |
12:15 |
Farbod Khoraminia The Netherlands
Deep Learning Unveils Molecular Footprints in Histology: Predicting Molecular Subtypes from Bladder Cancer Histology Slides |
CHAIRS
Mircea Serbanescu Romania
Nadieh Khalili The Netherlands
INVITED SPEAKER
Mircea Serbanescu Romania
Nadieh Khalili The Netherlands
INVITED SPEAKER
14:00 | Daniel Racoceanu France |
ABSTRACTS
14:20 |
Kastytis Sidlauskas United Kingdom
Predictive Morphological Markers for Ductal Carcinoma In Situ: A Computational Approach to Risk Stratification |
14:30 |
Adam Shephard United Kingdom
ODYN: An Artificial Intelligence-based Pipeline for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia |
14.40 |
Ana Leni Frei Switzerland
Using tumor topology to predict patient survival after neoadjuvant chemoradiotherapy in rectal cancer |
14:50 |
Jingsong Liu Germany
Diff-ST: Staining Translation between HE and IHC by Diffusion Models |
15:00 |
Mark Eastwood United Kingdom
Multi-task GNN Prediction in Breast Cancer using Deep Features and Cellular Composition Statistics |
15:10 |
Dominique Van Midden The Netherlands
Deep learning-based segmentation of peritubular capillaries in kidney transplant biopsies. |
15:20 |
Emilio Madrigal United States of America
The FHIR Standard and Digital Pathology: Accelerating Workflow Evolution |
15:30 |
Ujjwal Baid United States of America
Federated Learning for the Classification of Tumor Infiltrating Lymphocytes: One DL model for 12 cancer types |
15:40 |
Kesi Xu United Kingdom
Is Segment Anything Model Generalisable for Histology Images? |
15:50 |
Alboukadel Kassambara France
Attentive Deep-Learning model for predicting Immunoscore in TNBC from MyProbe RHU H&E images: histological interpretability and clinical outcomes |
PANELISTS
Naoko Tsuyama Japan
Inti Zlobec Switzerland
Michael Quick United States of America
Junya Fukuoka Japan
Norman Zerbe Germany