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Sinead Melsbach

Leiden University, Medical Center (LUMC), Department of Pathology

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Scholarly Papers (1)

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
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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.