A complete benchmark for polyp detection, segmentation and classification in colonoscopy images.

Autor: Tudela Y; Computer Vision Center and Computer Science Department, Universitat Autònoma de Cerdanyola del Valles, Barcelona, Spain., Majó M; Computer Vision Center and Computer Science Department, Universitat Autònoma de Cerdanyola del Valles, Barcelona, Spain., de la Fuente N; Computer Vision Center and Computer Science Department, Universitat Autònoma de Cerdanyola del Valles, Barcelona, Spain., Galdran A; Department of Information and Communication Technologies, SymBioSys Research Group, BCNMedTech, Barcelona, Spain., Krenzer A; Artificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians University of Würzburg, Würzburg, Germany., Puppe F; Artificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians University of Würzburg, Würzburg, Germany., Yamlahi A; Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany., Tran TN; Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany., Matuszewski BJ; Computer Vision and Machine Learning (CVML) Research Group, University of Central Lancashir (UCLan), Preston, United Kingdom., Fitzgerald K; Computer Vision and Machine Learning (CVML) Research Group, University of Central Lancashir (UCLan), Preston, United Kingdom., Bian C; Hebei University of Technology, Baoding, China., Pan J; Tianjin University, Tianjin, China., Liu S; Hebei University of Technology, Baoding, China., Fernández-Esparrach G; Digestive Endoscopy Unit, Hospital Clínic, Barcelona, Spain., Histace A; ETIS UMR 8051, École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA), Centre national de la recherche scientifique (CNRS), CY Paris Cergy University, Cergy, France., Bernal J; Computer Vision Center and Computer Science Department, Universitat Autònoma de Cerdanyola del Valles, Barcelona, Spain.

Publikováno v: Frontiers in oncology [Front Oncol] 2024 Sep 24; Vol. 14, pp. 1417862. Date of Electronic Publication: 2024 Sep 24 (Print Publication: 2024).

Integrated Simulation and Calibration Framework for Heating System Optimization.

Autor: Djebko K; Chair of Computer Science VI: Artificial Intelligence and Knowledge Systems, Julius-Maximilians-Universität Würzburg, Am Hubland, 97074 Würzburg, Germany., Weidner D; Chair of Computer Science VI: Artificial Intelligence and Knowledge Systems, Julius-Maximilians-Universität Würzburg, Am Hubland, 97074 Würzburg, Germany., Waleska M; Chair of Computer Science VI: Artificial Intelligence and Knowledge Systems, Julius-Maximilians-Universität Würzburg, Am Hubland, 97074 Würzburg, Germany., Krey T; ENER-IQ GmbH, Leightonstraße 3, 97074 Würzburg, Germany., Rausch S; ENER-IQ GmbH, Leightonstraße 3, 97074 Würzburg, Germany., Seipel D; Chair of Computer Science VI: Artificial Intelligence and Knowledge Systems, Julius-Maximilians-Universität Würzburg, Am Hubland, 97074 Würzburg, Germany., Puppe F; Chair of Computer Science VI: Artificial Intelligence and Knowledge Systems, Julius-Maximilians-Universität Würzburg, Am Hubland, 97074 Würzburg, Germany.

Publikováno v: Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Jan 29; Vol. 24 (3). Date of Electronic Publication: 2024 Jan 29.

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HIOP-Reader: Automated Data Extraction for the Analysis of Manually Recorded Nycthemeral IOPs and Glaucoma Progression

Autor: Puppe F, Schoen J, Agorastou, Dakroub M, Hillenkamp J, Loewen Na, Verma-Fuehring R

Purpose: Nycthemeral (24-hour) glaucoma inpatient intraocular pressure (IOP) monitoring has been used in Europe for more than 100 years to detect peaks missed during regular office hours. Data supporting this practice is lacking, partially because it

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b550d35eb10f3934701c18a9f3b2bfff
https://doi.org/10.20944/preprints202106.0571.v1

Automated classification of polyps using deep learning architectures and few-shot learning.

Autor: Krenzer A; Department of Artificial Intelligence and Knowledge Systems, Julius-Maximilians University of Würzburg, Sanderring 2, 97070, Würzburg, Germany. adrian.krenzer@uni-wuerzburg.de.; Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080, Würzburg, Germany. adrian.krenzer@uni-wuerzburg.de., Heil S; Department of Artificial Intelligence and Knowledge Systems, Julius-Maximilians University of Würzburg, Sanderring 2, 97070, Würzburg, Germany., Fitting D; Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080, Würzburg, Germany., Matti S; Department of Artificial Intelligence and Knowledge Systems, Julius-Maximilians University of Würzburg, Sanderring 2, 97070, Würzburg, Germany., Zoller WG; Department of Internal Medicine and Gastroenterology, Katharinenhospital, Kriegsbergstrasse 60, 70174, Stuttgart, Germany., Hann A; Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080, Würzburg, Germany., Puppe F; Department of Artificial Intelligence and Knowledge Systems, Julius-Maximilians University of Würzburg, Sanderring 2, 97070, Würzburg, Germany.

Publikováno v: BMC medical imaging [BMC Med Imaging] 2023 Apr 20; Vol. 23 (1), pp. 59. Date of Electronic Publication: 2023 Apr 20.