Publications of Leander van Eekelen

Papers in international journals

  1. L. van Eekelen, G. Litjens and K. Hebeda, "Artificial intelligence in bone marrow histological diagnostics: potential applications and challenges.", Pathobiology, 2023.
    Abstract DOI PMID Cited by ~2
  2. L. van Eekelen, H. Pinckaers, M. van den Brand, K. Hebeda and G. Litjens, "Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation.", Pathology, 2021.
    Abstract DOI PMID Download Cited by ~6

Papers in conference proceedings

  1. J. Spronck, T. Gelton, L. van Eekelen, J. Bogaerts, L. Tessier, M. van Rijthoven, L. van der Woude, M. van den Heuvel, W. Theelen, J. van der Laak and F. Ciompi, "nnUNet meets pathology: bridging the gap for application to whole-slide images and computational biomarkers", Medical Imaging with Deep Learning, 2023.
    Abstract Url
  2. J. Vermazeren, L. van Eekelen, L. Meesters, M. Looijen-Salamon, S. Vos, E. Munari, C. Mercan and F. Ciompi, "muPEN: Multi-class PseudoEdgeNet for PD-L1 assessment", Medical Imaging with Deep Learning, 2021.
    Abstract Url
  3. L. van Eekelen, H. Pinckaers, K. Hebeda and G. Litjens, "Multi-class semantic cell segmentation and classification of aplasia in bone marrow histology images", Medical Imaging, 2020;11320:113200B.
    Abstract DOI Cited by ~1

Abstracts

  1. L. van Eekelen, E. Munari, L. Meesters, G. de Souza, M. Demirel-Andishmand, D. Zegers, M. Looijen-Salamon, S. Vos and F. Ciompi, "Nuclei detection with YOLOv5 in PD-L1 stained non-small cell lung cancer whole slide images", European Congress of Pathology, 2022.
    Abstract
  2. L. van Eekelen, E. Munari, I. Girolami, A. Eccher, J. van der Laak, K. Grunberg, M. Looijen-Salamon, S. Vos and F. Ciompi, "Inter-rater agreement of pathologists on determining PD-L1 status in non-small cell lung cancer", European Congress of Pathology, 2022.
    Abstract
  3. J. Spronck, L. Eekelen, L. Tessier, J. Bogaerts, L. van der Woude, M. van den Heuvel, W. Theelen and F. Ciompi, "Deep learning-based quantification of immune infiltrate for predicting response to pembrolizumab from pre-treatment biopsies of metastatic non-small cell lung cancer: A study on the PEMBRO-RT phase II trial", Immuno-Oncology and Technology, 2022.
    Abstract

Master theses

  1. L. van Eekelen, "Deep learning-based analysis of bone marrow histopathology images", Master thesis, 2020.
    Abstract