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David L Hölscher

Showing results (1-10 of 10) with videos related to

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Pflugers Archiv : European Journal of Physiology|August 2, 2024
Decoding pathology: the role of computational pathology in research and diagnosticsDavid L Hölscher, Roman D Bülow
Kidney International|September 21, 2025
Advances in computational nephropathologyDavid L Hölscher, Roman D Bülow, Martin Strauch, et al.
NPJ Systems Biology and Applications|August 7, 2023
Extending the landscape of omics technologies by pathomicsRoman D Bülow, David L Hölscher, Ivan G Costa, et al.
Journal of Pathology Informatics|October 21, 2022
Tackling stain variability using CycleGAN-based stain augmentationNassim Bouteldja, David L Hölscher, Roman D Bülow, et al.
BMC Bioinformatics|March 5, 2024
tRigon: an R package and Shiny App for integrative (path-)omics data analysisDavid L Hölscher, Michael Goedertier, Barbara M Klinkhammer, et al.
The Lancet. Digital Health|November 23, 2023
Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling studyAlireza Vafaei Sadr, Roman Bülow, Saskia von Stillfried, et al.
NPJ Digital Medicine|December 24, 2024
Ecologically sustainable benchmarking of AI models for histopathologyYu-Chia Lan, Martin Strauch, Pourya Pilva, et al.
Molecular Systems Biology|January 4, 2024
Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)Mehdi Joodaki, Mina Shaigan, Victor Parra, et al.
Nature Communications|January 28, 2023
Next-Generation Morphometry for pathomics-data mining in histopathologyDavid L Hölscher, Nassim Bouteldja, Mehdi Joodaki, et al.
The Lancet. Digital Health|November 19, 2021
Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept studyJesper Kers, Roman D Bülow, Barbara M Klinkhammer, et al.
Pageof 1

Showing results (1-10 of 10) with videos related to

Sort By:
Pageof 1
Pflugers Archiv : European Journal of Physiology|August 2, 2024
Decoding pathology: the role of computational pathology in research and diagnosticsDavid L Hölscher, Roman D Bülow
Kidney International|September 21, 2025
Advances in computational nephropathologyDavid L Hölscher, Roman D Bülow, Martin Strauch, et al.
NPJ Systems Biology and Applications|August 7, 2023
Extending the landscape of omics technologies by pathomicsRoman D Bülow, David L Hölscher, Ivan G Costa, et al.
Journal of Pathology Informatics|October 21, 2022
Tackling stain variability using CycleGAN-based stain augmentationNassim Bouteldja, David L Hölscher, Roman D Bülow, et al.
BMC Bioinformatics|March 5, 2024
tRigon: an R package and Shiny App for integrative (path-)omics data analysisDavid L Hölscher, Michael Goedertier, Barbara M Klinkhammer, et al.
The Lancet. Digital Health|November 23, 2023
Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling studyAlireza Vafaei Sadr, Roman Bülow, Saskia von Stillfried, et al.
NPJ Digital Medicine|December 24, 2024
Ecologically sustainable benchmarking of AI models for histopathologyYu-Chia Lan, Martin Strauch, Pourya Pilva, et al.
Molecular Systems Biology|January 4, 2024
Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)Mehdi Joodaki, Mina Shaigan, Victor Parra, et al.
Nature Communications|January 28, 2023
Next-Generation Morphometry for pathomics-data mining in histopathologyDavid L Hölscher, Nassim Bouteldja, Mehdi Joodaki, et al.
The Lancet. Digital Health|November 19, 2021
Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept studyJesper Kers, Roman D Bülow, Barbara M Klinkhammer, et al.
Pageof 1