Zobrazeno 1 - 7
of 7
pro vyhledávání: '"David L. Hölscher"'
Autor:
David L. Hölscher, Michael Goedertier, Barbara M. Klinkhammer, Patrick Droste, Ivan G. Costa, Peter Boor, Roman D. Bülow
Publikováno v:
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-11 (2024)
Abstract Background Pathomics facilitates automated, reproducible and precise histopathology analysis and morphological phenotyping. Similar to molecular omics, pathomics datasets are high-dimensional, but also face large outlier variability and inhe
Externí odkaz:
https://doaj.org/article/b0d0aa69d8504016b2e20a8120906e4d
Publikováno v:
npj Systems Biology and Applications, Vol 9, Iss 1, Pp 1-3 (2023)
Externí odkaz:
https://doaj.org/article/73bbfbcb523746e588c6a20fdef6a5b4
Autor:
David L. Hölscher, Nassim Bouteldja, Mehdi Joodaki, Maria L. Russo, Yu-Chia Lan, Alireza Vafaei Sadr, Mingbo Cheng, Vladimir Tesar, Saskia V. Stillfried, Barbara M. Klinkhammer, Jonathan Barratt, Jürgen Floege, Ian S. D. Roberts, Rosanna Coppo, Ivan G. Costa, Roman D. Bülow, Peter Boor
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-14 (2023)
Pathology diagnostics still rely on tissue morphology assessment by trained experts. Here, the authors perform deep-learning-based segmentation followed by large-scale feature extraction of histological images, i.e., next-generation morphometry, to e
Externí odkaz:
https://doaj.org/article/23a6ad923ea34d429c8664558993fafc
Autor:
Nassim Bouteldja, David L. Hölscher, Roman D. Bülow, Ian S.D. Roberts, Rosanna Coppo, Peter Boor
Publikováno v:
Journal of Pathology Informatics, Vol 13, Iss , Pp 100140- (2022)
Background: Considerable inter- and intra-laboratory stain variability exists in pathology, representing a challenge in development and application of deep learning (DL) approaches. Since tackling all sources of stain variability with manual annotati
Externí odkaz:
https://doaj.org/article/d241d70955614405ab441fd9e1530ec1
Publikováno v:
Die Nephrologie. 17:369-375
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2524fbb001ac707167ef3a9f26f5f1d0
https://doi.org/10.1007/s11560-022-00598-3
https://doi.org/10.1007/s11560-022-00598-3
Autor:
Garry L. Corthals, Frederike J. Bemelman, Francesco Fontana, Azam S Nurmohamed, Arjan D. van Zuilen, Maarten Naesens, Peter Boor, Jürgen Floege, Gerben E. Breimer, Tri Q. Nguyen, Joris J. T. H. Roelofs, Adeyemi Adefidipe Abiola, Barbara M. Klinkhammer, Saskia von Stillfried, Hessel Peters-Sengers, Sonja Djudjaj, Tobias T Pieters, Rianne Hofstraat, Roman D. Bülow, Sandrine Florquin, Jesper Kers, Jakob Nikolas Kather, David L Hölscher
Publikováno v:
The Lancet Digital Health, 4(1), 18-26. ELSEVIER
The Lancet Digital Health, 4(1), e18-e26. Elsevier Ltd
The lancet / Digital health 4(1), e18-e26 (2022). doi:10.1016/S2589-7500(21)00211-9
Kers, J, Bülow, R D, Klinkhammer, B M, Breimer, G E, Fontana, F, Abiola, A A, Hofstraat, R, Corthals, G L, Peters-Sengers, H, Djudjaj, S, von Stillfried, S, Hölscher, D L, Pieters, T T, van Zuilen, A D, Bemelman, F J, Nurmohamed, A S, Naesens, M, Roelofs, J J T H, Florquin, S, Floege, J, Nguyen, T Q, Kather, J N & Boor, P 2022, ' Deep learning-based classification of kidney transplant pathology : a retrospective, multicentre, proof-of-concept study ', The Lancet Digital Health, vol. 4, no. 1, pp. e18-e26 . https://doi.org/10.1016/S2589-7500(21)00211-9
The Lancet Digital Health, 4(1), e18-e26. Elsevier Ltd
The lancet / Digital health 4(1), e18-e26 (2022). doi:10.1016/S2589-7500(21)00211-9
Kers, J, Bülow, R D, Klinkhammer, B M, Breimer, G E, Fontana, F, Abiola, A A, Hofstraat, R, Corthals, G L, Peters-Sengers, H, Djudjaj, S, von Stillfried, S, Hölscher, D L, Pieters, T T, van Zuilen, A D, Bemelman, F J, Nurmohamed, A S, Naesens, M, Roelofs, J J T H, Florquin, S, Floege, J, Nguyen, T Q, Kather, J N & Boor, P 2022, ' Deep learning-based classification of kidney transplant pathology : a retrospective, multicentre, proof-of-concept study ', The Lancet Digital Health, vol. 4, no. 1, pp. e18-e26 . https://doi.org/10.1016/S2589-7500(21)00211-9
The lancet / Digital health 4(1), e18-e26 (2022). doi:10.1016/S2589-7500(21)00211-9
Published by The Lancet, London
Published by The Lancet, London
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::370ca89d866b38a88d146e9bf428e219
https://hdl.handle.net/11245.1/2ff8e5a4-88df-48d5-95e1-dd2621f152bb
https://hdl.handle.net/11245.1/2ff8e5a4-88df-48d5-95e1-dd2621f152bb
Autor:
Mehdi Joodaki, Mina Shaigan, Victor Parra, Roman D Bülow, Christoph Kuppe, David L Hölscher, Mingbo Cheng, James S Nagai, Michaël Goedertier, Nassim Bouteldja, Vladimir Tesar, Jonathan Barratt, Ian SD Roberts, Rosanna Coppo, Rafael Kramann, Peter Boor, Ivan G Costa
Publikováno v:
Molecular Systems Biology, Vol 20, Iss 2, Pp 57-74 (2023)
Abstract Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated
Externí odkaz:
https://doaj.org/article/ceee0a1c3cb64384992c14d626f0daf2