Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study
2021 · The Lancet Digital Health · 113 citations
https://doi.org/10.1016/s2589-7500(21)00211-9
D
David L. Hölscher
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9
h-index
8
i10-index
David L. Hölscher is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: AI in cancer detection & Radiomics and Machine Learning in Medical Imaging & Chronic Kidney Disease and Diabetes. The author has an h-index of 9, co-authored 17 publications.
ORCID: 0000-0001-8595-0694Papers by this author
worksOperational greenhouse-gas emissions of deep learning in digital pathology: a modelling study
2023 · The Lancet Digital Health · 32 citations
https://doi.org/10.1016/s2589-7500(23)00219-4
Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)
2023 · Molecular Systems Biology · 28 citations
https://doi.org/10.1038/s44320-023-00003-8
Stain-Independent Deep Learning–Based Analysis of Digital Kidney Histopathology
2022 · American Journal Of Pathology · 17 citations
https://doi.org/10.1016/j.ajpath.2022.09.011
Tackling stain variability using CycleGAN-based stain augmentation
2022 · Journal of Pathology Informatics · 14 citations
https://doi.org/10.1016/j.jpi.2022.100140
Ecologically sustainable benchmarking of AI models for histopathology
2024 · npj Digital Medicine · 11 citations
https://doi.org/10.1038/s41746-024-01397-x
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