Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Nassim Bouteldja"'
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
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:
Simone Gaedicke, Gabriele Niedermann, Anne Grote, Friedrich Feuerhake, Eva Oswald, Kanstantsin Lashuk, Daniel Bug, Nassim Bouteldja, Dorothee Lenhard, Anne Löhr, Anke Behnke, Volker Knauff, Anna Edinger, Kerstin Klingner, Dorit Merhof, Julia Schueler
Publikováno v:
Journal for ImmunoTherapy of Cancer, Vol 10, Iss 4 (2022)
Externí odkaz:
https://doaj.org/article/6d293a6cd46e42e59c4af0d350d0d410
Publikováno v:
Journal of Pathology Informatics, Vol 13, Iss , Pp 100107- (2022)
Background: In digital pathology, many image analysis tasks are challenged by the need for large and time-consuming manual data annotations to cope with various sources of variability in the image domain. Unsupervised domain adaptation based on image
Externí odkaz:
https://doaj.org/article/7d0d75183957439c8049812dfbfcffc9
Autor:
Laxmi Gupta, Barbara Mara Klinkhammer, Claudia Seikrit, Nina Fan, Nassim Bouteldja, Philipp Gräbel, Michael Gadermayr, Peter Boor, Dorit Merhof
Publikováno v:
Journal of Pathology Informatics, Vol 13, Iss , Pp 100097- (2022)
Whole slide images contain a magnitude of quantitative information that may not be fully explored in qualitative visual assessments. We propose: (1) a novel pipeline for extracting a comprehensive set of visual features, which are detectable by a pat
Externí odkaz:
https://doaj.org/article/4a8e371616cd430789dad3ac2a70336f
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
Autor:
Christian Lucas, André Kemmling, Nassim Bouteldja, Linda F. Aulmann, Amir Madany Mamlouk, Mattias P. Heinrich
Publikováno v:
Frontiers in Neurology, Vol 9 (2018)
Cerebrovascular diseases, in particular ischemic stroke, are one of the leading global causes of death in developed countries. Perfusion CT and/or MRI are ideal imaging modalities for characterizing affected ischemic tissue in the hyper-acute phase.
Externí odkaz:
https://doaj.org/article/01e8d31492f943a09db34b5ce7c5792b
Autor:
Barbara Mara Klinkhammer, Simone Buchtler, Sonja Djudjaj, Nassim Bouteldja, Runolfur Palsson, Vidar Orn Edvardsson, Margret Thorsteinsdottir, Jürgen Floege, Matthias Mack, Peter Boor
Publikováno v:
Kidney International. 102:307-320
Although underlying mechanisms and the clinical course of kidney disease progression are well described, less is known about potential disease reversibility. Therefore, to analyze kidney recovery, we adapted a commonly used murine chronic kidney dise
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90bb98e343b58d8b26998e148bfe4f7e
https://doi.org/10.1016/j.kint.2022.02.039
https://doi.org/10.1016/j.kint.2022.02.039
Autor:
Nassim Bouteldja, David Laurin Hölscher, Barbara Mara Klinkhammer, Roman David Buelow, Johannes Lotz, Nick Weiss, Christoph Daniel, Kerstin Amann, Peter Boor
Publikováno v:
The American journal of pathology.
Convolutional neural network (CNN)-based image analysis applications in digital pathology (eg, tissue segmentation) require a large amount of annotated data and are mostly trained and applicable on a single stain. Here, a novel concept based on stain
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a01e06a34454cb6cf979ea610172649
https://pubmed.ncbi.nlm.nih.gov/36309103
https://pubmed.ncbi.nlm.nih.gov/36309103
Autor:
David Hölscher, Nassim Bouteldja, Yu-Chia Lan, Saskia von Stillfried, Roman David Bülow, Peter Boor
Publikováno v:
Nephrology Dialysis Transplantation. 37
BACKGROUND AND AIMS Nephropathology is essential for the diagnosis of kidney diseases. Deep learning-based image analyses, including segmentation of kidney histology, open new possibilities for reproducible quantitative precision pathology. Current s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b39b67d1aea08015eb73ceeb51cde85
https://doi.org/10.1093/ndt/gfac063.014
https://doi.org/10.1093/ndt/gfac063.014