Deep learning-based segmentation in MRI-(immuno)histological examination of myelin and axonal damage in normal-appearing white matter and white matter hyperintensities.

Autor: Solé-Guardia G; Department of Medical Imaging, Anatomy, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIME, Radboud Alzheimer Center, Nijmegen, The Netherlands., Luijten M; Department of Medical Imaging, Anatomy, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIME, Radboud Alzheimer Center, Nijmegen, The Netherlands., Janssen E; Department of Medical Imaging, Anatomy, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIME, Radboud Alzheimer Center, Nijmegen, The Netherlands., Visch R; Department of Medical Imaging, Anatomy, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIME, Radboud Alzheimer Center, Nijmegen, The Netherlands., Geenen B; Department of Medical Imaging, Anatomy, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIME, Radboud Alzheimer Center, Nijmegen, The Netherlands., Küsters B; Department of Pathology, Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, The Netherlands., Claassen JAHR; Department of Geriatrics, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Radboud Alzheimer Center, Nijmegen, The Netherlands.; Department of Cardiovascular Sciences, University of Leicester, Leicester, UK., Litjens G; Department of Pathology, Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, The Netherlands.; Computational Pathology Group, Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, The Netherlands., de Leeuw FE; Department of Neurology, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Nijmegen, The Netherlands., Wiesmann M; Department of Medical Imaging, Anatomy, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIME, Radboud Alzheimer Center, Nijmegen, The Netherlands., Kiliaan AJ; Department of Medical Imaging, Anatomy, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIME, Radboud Alzheimer Center, Nijmegen, The Netherlands.

Publikováno v: Brain pathology (Zurich, Switzerland) [Brain Pathol] 2024 Aug 23, pp. e13301. Date of Electronic Publication: 2024 Aug 23.

Ferumoxtran-10-enhanced MRI for pre-operative metastatic lymph node detection in pancreatic, duodenal, or periampullary adenocarcinoma.

Autor: Litjens G; Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands. g.litjens@radboudumc.nl., Nakamoto A; Department of Radiology, Osaka University Graduate School of Medicine, Suita, Japan., Brosens LAA; Department of Pathology, Radboud University Medical Center, Nijmegen, University Medical Center Utrecht, Utrecht, The Netherlands., Maas MC; Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands., Scheenen TWJ; Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands., Zámecnik P; Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands., van Geenen EJM; Department of Gastroenterology and Hepatology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands., Prokop M; Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands., van Laarhoven KJHM; Department of Surgery, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands., Hermans JJ; Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.

Publikováno v: European radiology [Eur Radiol] 2024 Dec; Vol. 34 (12), pp. 7973-7984. Date of Electronic Publication: 2024 Jun 22.

Evaluation of Artificial Intelligence-Based Gleason Grading Algorithms "in the Wild".

Autor: Faryna K; Radboud University Medical Center, Computational Pathology Group, Nijmegen, The Netherlands. Electronic address: khrystyna.faryna@radboudumc.nl., Tessier L; Radboud University Medical Center, Computational Pathology Group, Nijmegen, The Netherlands., Retamero J; Paige, New York, New York., Bonthu S; AIRA Matrix, Thane, India., Samanta P; AIRA Matrix, Thane, India., Singhal N; AIRA Matrix, Thane, India., Kammerer-Jacquet SF; Department of Pathology, Rennes University Hospital, Rennes, France., Radulescu C; Department of Pathological Anatomy and Cytology, Hopital Foch, Suresnes, France., Agosti V; Department of Medicine and Surgery, University of Brescia, Brescia, Italy., Collin A; Department of Cell and Tissue Pathology, Angers University Hospital Center, Angers, France., Farre X; Public Health Agency of Catalonia, Lleida, Spain., Fontugne J; Department of Pathology, Institut Curie, Saint-Cloud, France., Grobholz R; Institute of Pathology, Cantonal Hospital Aarau, Aarau, Switzerland., Hoogland AM; Department of Pathology, Isala Zwolle, Zwolle, The Netherlands., Moreira Leite KR; Department of Surgery, Faculty of Medicine of the University of Sao Paulo, Sao Paulo, Brazil., Oktay M; Department of Pathology, Memorial Hospitals Group, Istanbul, Turkey., Polonia A; Department of Pathology, Ipatimup, Porto, Portugal., Roy P; Department of Pathology, Tata Medical Center, Kolkata, India., Salles PG; Teaching and Research Center, Instituto Mario Penna, Belo Horizonte, Brazil., van der Kwast TH; Department of Anatomic Pathology, University Health Network and Princess Margaret Cancer Center, Toronto, Canada., van Ipenburg J; Radboud University Medical Center, Computational Pathology Group, Nijmegen, The Netherlands., van der Laak J; Radboud University Medical Center, Computational Pathology Group, Nijmegen, The Netherlands; Center for Medical Image Science and Visualization, Linkoping University, Linkoping, Sweden., Litjens G; Radboud University Medical Center, Computational Pathology Group, Nijmegen, The Netherlands.

Publikováno v: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc [Mod Pathol] 2024 Nov; Vol. 37 (11), pp. 100563. Date of Electronic Publication: 2024 Jul 16.

Transformer-Based Weakly Supervised Learning for Whole Slide Lung Cancer Image Classification.

Autor: An J, Wang Y, Cai Q, Zhao G, Dooper S, Litjens G, Gao Z

Publikováno v: IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2024 Jul 09; Vol. PP. Date of Electronic Publication: 2024 Jul 09.

A comprehensive study on partial shading response of c-Si modules and yield modeling of string inverter and module level power electronics

Autor: Sinapis, K. ^{a, ⁎}, Tzikas, C. ^{a, c}, Litjens, G. ^{a, b}, van den Donker, M. ^a, Folkerts, W. ^a, van Sark, W.G.J.H.M. ^b, Smets, A. ^c

Publikováno v: In Solar Energy October 2016 135:731-741