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Dipòsit Digital de Documents de la UAB	2 registres trobats	La cerca s'ha fet en 0.25 segons.

12 p, 2.9 MB

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation : The MMs Challenge / Campello, Victor M. (Universitat de Barcelona. Departament de Matemàtiques i Informàtica) ; Gkontra, Polyxeni (Universitat de Barcelona. Departament de Matemàtiques i Informàtica) ; Izquierdo, Cristian (Universitat de Barcelona. Departament de Matemàtiques i Informàtica) ; Martin-Isla, Carlos (Universitat de Barcelona. Departament de Matemàtiques i Informàtica) ; Sojoudi, Alireza (Circle Cardiovascular Imaging Pvt. Ltd.) ; Full, Peter M. (German Cancer Research Center) ; Maier-Hein, Klaus (Division of Medical Image Computing. German Cancer Research Center) ; Zhang, Yao (Chinese Academy of Sciences. Institute of Computing Technology) ; He, Zhiqiang (Lenovo Ltd.) ; Ma, Jun (Nanjing University of Science and Technology) ; Parreno, Mario (Universitat Politècnica de València) ; Albiol, Alberto (Universitat Politècnica de València. iTeam Research Institute) ; Kong, Fanwei (University of California at Berkeley. Department of Mechanical Engineering) ; Shadden, Shawn C. (University of California at Berkeley. Department of Mechanical Engineeringy) ; Corral Acero, Jorge (Institute of Biomedical Engineering. Department of Engineering Science. University of Oxford) ; Sundaresan, Vaanathi (Nuffield Department of Clinical Neurosciences. Centre for the Functional MRI of the Brain. University of Oxford) ; Saber, Mina (Research and Development Division. Intixel Company S.A.E.) ; Elattar, Mustafa (Research and Development Division. Intixel Company S.A.E.) ; Li, Hongwei (Department of Computer Science. Technische Universität München) ; Menze, Bjoern (Department of Computer Science. Technische Universität München) ; Khader, Firas (ARISTRA GmbH) ; Haarburger, Christoph (ARISTRA GmbH) ; Scannell, Cian M. (School of Biomedical Engineering and Imaging Sciences. King's College London) ; Veta, Mitko (Department of Biomedical Engineering. Eindhoven University of Technology) ; Carscadden, Adam (Department of Radiology and Diagnostic Imaging. University of Alberta) ; Punithakumar, Kumaradevan (Department of Radiology and Diagnostic Imaging. University of Alberta) ; Liu, Xiao (School of Engineering. The University of Edinburgh) ; Tsaftaris, Sotirios A. (School of Engineering. The University of Edinburgh) ; Huang, Xiaoqiong (School of Biomedical Engineering. Shenzhen University) ; Yang, Xin (School of Biomedical Engineering. Shenzhen University) ; Li, Lei (School of Biomedical Engineering. Shenzhen University) ; Zhuang, Xiahai (School of Data Science. Fudan University) ; Viladés Medel, David (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya)) ; Descalzo, Martin (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya)) ; Guala, Andrea (Hospital Universitari Vall d'Hebron. Institut de Recerca) ; Mura, Lucía La (Department of Advanced Biomedical Sciences. University of Naples Federico II) ; Friedrich, Matthias G. (Department of Medicine and Diagnostic Radiology. McGill University) ; Garg, Ria (Department of Medicine and Diagnostic Radiology. McGill University) ; Lebel, Julie (Department of Medicine and Diagnostic Radiology. McGill University) ; Henriques, Filipe. (Department of Cardiology. University Heart Vascular Center Hamburg) ; Karakas, Mahir (Department of Cardiology. University Heart Vascular Center Hamburg) ; Cavus, Ersin (Barts Heart Centre. Barts Health NHS Trust) ; Petersen, Steffen E. (Universitat de Barcelona. Departament de Matemàtiques i Informàtica) ; Escalera, Sergio (Hospital Universitari Vall d'Hebron. Institut de Recerca) ; Segui, Santi (Hospital Universitari Vall d'Hebron. Institut de Recerca) ; Rodriguez-Palomares, Jose F.. (Universitat de Barcelona. Departament de Matemàtiques i Informàtica) ; Lekadir, Karim (Universitat de Barcelona. Departament de Matemàtiques i Informàtica) ; Universitat Autònoma de Barcelona
The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. [...]
2021 - DOI (Digital Object Identifier)

10.1109/TMI.2021.3090082
IEEE Transactions on Medical Imaging, Vol. 40 Núm. 12 (january 2021) , p. 3543-3554

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12 p, 7.7 MB

Mosaic-Based Color-Transform Optimization for Lossy and Lossy-to-Lossless Compression of Pathology Whole-Slide Images / Hernández-Cabronero, Miguel (University of Warwick) ; Sánchez, Victor (University of Warwick) ; Marcellin, Michael W. (University of Arizona) ; Aulí Llinàs, Francesc (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions) ; Blanes Garcia, Ian (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions) ; Serra Sagristà, Joan (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
The use of whole-slide images (WSIs) in pathology entails stringent storage and transmission requirements because of their huge dimensions. Therefore, image compression is an essential tool to enable efficient access to these data. [...]
2019 - DOI (Digital Object Identifier)

10.1109/TMI.2018.2852685
IEEE Transactions on Medical Imaging, Vol. 38, Issue 1 (January 2019) , p. 21-32

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