| Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation L van Eekelen, H Pinckaers, M van den Brand, KM Hebeda, G Litjens Pathology 54 (3), 318-327, 2022 | 12 | 2022 |
| Artificial intelligence in bone marrow histological diagnostics: potential applications and challenges L van Eekelen, G Litjens, KM Hebeda Pathobiology 91 (1), 8-17, 2024 | 3 | 2024 |
| Multi-class semantic cell segmentation and classification of aplasia in bone marrow histology images L van Eekelen, H Pinckaers, KM Hebeda, G Litjens Medical Imaging 2020: Digital Pathology 11320, 68-75, 2020 | 3 | 2020 |
| nnUNet meets pathology: bridging the gap for application to whole-slide images and computational biomarkers J Spronck, T Gelton, L van Eekelen, J Bogaerts, L Tessier, ... Medical Imaging with Deep Learning, 2023 | 2 | 2023 |
| Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images L van Eekelen, J Spronck, M Looijen-Salamon, S Vos, E Munari, ... Scientific Reports 14 (1), 7136, 2024 | 1 | 2024 |
| PEN: Multi-class PseudoEdgeNet for PD-L1 assessment J Vermazeren, L van Eekelen, LD Meesters, M Looijen-Salamon, S Vos, ... Medical Imaging with Deep Learning, 2021 | 1 | 2021 |
| Inter-rater agreement of pathologists on determining cell-level PD-L1 status in non-small cell lung cancer L van Eekelen, E Munari, I Girolami, A Eccher, J van der Laak, ... VIRCHOWS ARCHIV 481 (SUPPL 1), S79-S79, 2022 | | 2022 |
| Nuclei detection with YOLOv5 in PD-L1 stained non-small cell lung cancer whole-slide images L van Eekelen, E Munari, LD Meesters, GS de Souza, ... VIRCHOWS ARCHIV 481 (SUPPL 1), S79-S80, 2022 | | 2022 |
| Basal cell carcinoma detection using weakly supervised deep learning methods and rule-based labels D Geijs, S Dooper, W Aswolinskiy, L van Eekelen, A Amir, G Litjens | | 2022 |
