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Jouke Dijkstra

Leiden University - Department of Vascular and Molecular Imaging

SCHOLARLY PAPERS

2

DOWNLOADS

208

TOTAL CITATIONS

1

Scholarly Papers (2)

1.

Interpretable Deep Learning Predicts the Molecular Endometrial Cancer Classification from H&E Images: A Combined Analysis of the Portec Randomized Clinical Trials

Number of pages: 25 Posted: 24 Jun 2022
Leiden University, Medical Center (LUMC), Department of Pathology, University Hospital Zurich - Department of Pathology and Molecular Pathology, Leiden University, Medical Center (LUMC), Department of Pathology, Leiden University - Department of Vascular and Molecular Imaging, Leiden University, Medical Center (LUMC), Department of Pathology, Medisch Spectrum Twente - Department of Radiation Oncology, Laboratorium Pathologie Oost-Nederland (LabPON) - Department of Pathology, Laboratorium Pathologie Oost-Nederland (LabPON) - Department of Pathology, University Medical Center Utrecht - Department of Radiation Oncology, Maastricht University - Department of Radiation Oncology (MAASTRO), Erasmus University Medical Center Rotterdam - Department of Radiation Oncology, Radiotherapiegroep - Department of Radiation Oncology, Leiden University - Department of Radiation Oncology, Government of the United Kingdom - Department of Clinical Oncology, Barts Health NHS Trust, Department of Cellular Pathology, Peter MacCallum Cancer Centre - Department of Medical Oncology, University of Toronto - Department of Medical Oncology and Hematology, Université Paris XI Sud - Gustave Roussy Cancer Campus, University of Groningen - Department of Obstetrics and Gynecology, Leiden University, Medical Center (LUMC), Department of Pathology, Leiden University - Department of Radiation Oncology, Leiden University - Department of Radiation Oncology, University Hospital Zurich - Department of Pathology and Molecular Pathology and Leiden University, Medical Center (LUMC), Department of Pathology
Downloads 130 (558,171)
Citation 1

Abstract:

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deep learning, Endometrial Cancer, Molecular classification, Morphological features, Prognostic refinement, POLEmut EC, MMRd EC, NSMP EC, p53abn EC, whole slide images, Histopathology images.

2.

Machine Learning-Based Analysis of Coronary Ct Angiography Radiomic Features Improves the Identification of Functionally Significant Lesions

Number of pages: 25 Posted: 04 Mar 2025
affiliation not provided to SSRN, National and Kapodistrian University of Athens, Ospedale Santa Chiara - Azienda Ospedaliero Universitaria Pisana, affiliation not provided to SSRN, National and Kapodistrian University of Athens, Leiden University, Leiden University - Department of Vascular and Molecular Imaging, National and Kapodistrian University of Athens, University of Patras, University of Ioannina, National and Kapodistrian University of Athens, National and Kapodistrian University of Athens, affiliation not provided to SSRN, Imperial College London, Ospedale Santa Chiara - Azienda Ospedaliero Universitaria Pisana, affiliation not provided to SSRN, University of Ioannina, affiliation not provided to SSRN, Fondazione Toscana Gabriele Monasterio and affiliation not provided to SSRN
Downloads 78 (816,709)

Abstract:

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Coronary Artery Disease, Myocardial perfusion imaging, Coronary CT Angiography (CCTA), Radiomics, Coronary Plaque