RIS Samples

sample_embase.ris

TY  - JOUR
M3  - Erratum
Y1  - 2023
VL  - 14
IS  - 1
SN  - 2041-1723
JF  - Nature Communications
JO  - Nat. Commun.
UR  - https://www.embase.com/search/results?subaction=viewrecord&id=L2021258467&from=export
U2  - L2021258467
C5  - 36702828
DB  - Embase
DB  - Medline
U4  - 2023-01-31
L2  - http://dx.doi.org/10.1038/s41467-023-36188-7
DO  - 10.1038/s41467-023-36188-7
LK  - https://slsp-unige.primo.exlibrisgroup.com/openurl/41SLSP_UGE/41SLSP_UGE:VU1?sid=EMBASE&sid=EMBASE&issn=20411723&id=doi:10.1038%2Fs41467-023-36188-7&atitle=Author+Correction%3A+Federated+learning+enables+big+data+for+rare+cancer+boundary+detection+%28Nature+Communications%2C+%282022%29%2C+13%2C+1%2C+%287346%29%2C+10.1038%2Fs41467-022-33407-5%29&stitle=Nat.+Commun.&title=Nature+Communications&volume=14&issue=1&spage=&epage=&aulast=Pati&aufirst=Sarthak&auinit=S.&aufull=Pati+S.&coden=&isbn=&pages=-&date=2023&auinit1=S&auinitm=
A1  - Pati, S.
A1  - Baid, U.
A1  - Edwards, B.
A1  - Sheller, M.
A1  - Wang, S.-H.
A1  - Reina, G.A.
A1  - Foley, P.
A1  - Gruzdev, A.
A1  - Karkada, D.
A1  - Davatzikos, C.
A1  - Sako, C.
A1  - Ghodasara, S.
A1  - Bilello, M.
A1  - Mohan, S.
A1  - Vollmuth, P.
A1  - Brugnara, G.
A1  - Preetha, C.J.
A1  - Sahm, F.
A1  - Maier-Hein, K.
A1  - Zenk, M.
A1  - Bendszus, M.
A1  - Wick, W.
A1  - Calabrese, E.
A1  - Rudie, J.
A1  - Villanueva-Meyer, J.
A1  - Cha, S.
A1  - Ingalhalikar, M.
A1  - Jadhav, M.
A1  - Pandey, U.
A1  - Saini, J.
A1  - Garrett, J.
A1  - Larson, M.
A1  - Jeraj, R.
A1  - Currie, S.
A1  - Frood, R.
A1  - Fatania, K.
A1  - Huang, R.Y.
A1  - Chang, K.
A1  - Balaña, C.
A1  - Capellades, J.
A1  - Puig, J.
A1  - Trenkler, J.
A1  - Pichler, J.
A1  - Necker, G.
A1  - Haunschmidt, A.
A1  - Meckel, S.
A1  - Shukla, G.
A1  - Liem, S.
A1  - Alexander, G.S.
A1  - Lombardo, J.
A1  - Palmer, J.D.
A1  - Flanders, A.E.
A1  - Dicker, A.P.
A1  - Sair, H.I.
A1  - Jones, C.K.
A1  - Venkataraman, A.
A1  - Jiang, M.
A1  - So, T.Y.
A1  - Chen, C.
A1  - Heng, P.A.
A1  - Dou, Q.
A1  - Kozubek, M.
A1  - Lux, F.
A1  - Michálek, J.
A1  - Matula, P.
A1  - Keřkovský, M.
A1  - Kopřivová, T.
A1  - Dostál, M.
A1  - Vybíhal, V.
A1  - Vogelbaum, M.A.
A1  - Mitchell, J.R.
A1  - Farinhas, J.
A1  - Maldjian, J.A.
A1  - Yogananda, C.G.B.
A1  - Pinho, M.C.
A1  - Reddy, D.
A1  - Holcomb, J.
A1  - Wagner, B.C.
A1  - Ellingson, B.M.
A1  - Cloughesy, T.F.
A1  - Raymond, C.
A1  - Oughourlian, T.
A1  - Hagiwara, A.
A1  - Wang, C.
A1  - To, M.-S.
A1  - Bhardwaj, S.
A1  - Chong, C.
A1  - Agzarian, M.
A1  - Falcão, A.X.
A1  - Martins, S.B.
A1  - Teixeira, B.C.A.
A1  - Sprenger, F.
A1  - Menotti, D.
A1  - Lucio, D.R.
A1  - LaMontagne, P.
A1  - Marcus, D.
A1  - Wiestler, B.
A1  - Kofler, F.
A1  - Ezhov, I.
A1  - Metz, M.
A1  - Jain, R.
A1  - Lee, M.
A1  - Lui, Y.W.
A1  - McKinley, R.
A1  - Slotboom, J.
A1  - Radojewski, P.
A1  - Meier, R.
A1  - Wiest, R.
A1  - Murcia, D.
A1  - Fu, E.
A1  - Haas, R.
A1  - Thompson, J.
A1  - Ormond, D.R.
A1  - Badve, C.
A1  - Sloan, A.E.
A1  - Vadmal, V.
A1  - Waite, K.
A1  - Colen, R.R.
A1  - Pei, L.
A1  - Ak, M.
A1  - Srinivasan, A.
A1  - Bapuraj, J.R.
A1  - Rao, A.
A1  - Wang, N.
A1  - Yoshiaki, O.
A1  - Moritani, T.
A1  - Turk, S.
A1  - Lee, J.
A1  - Prabhudesai, S.
A1  - Morón, F.
A1  - Mandel, J.
A1  - Kamnitsas, K.
A1  - Glocker, B.
A1  - Dixon, L.V.M.
A1  - Williams, M.
A1  - Zampakis, P.
A1  - Panagiotopoulos, V.
A1  - Tsiganos, P.
A1  - Alexiou, S.
A1  - Haliassos, I.
A1  - Zacharaki, E.I.
A1  - Moustakas, K.
A1  - Kalogeropoulou, C.
A1  - Kardamakis, D.M.
A1  - Choi, Y.S.
A1  - Lee, S.-K.
A1  - Chang, J.H.
A1  - Ahn, S.S.
A1  - Luo, B.
A1  - Poisson, L.
A1  - Wen, N.
A1  - Tiwari, P.
A1  - Verma, R.
A1  - Bareja, R.
A1  - Yadav, I.
A1  - Chen, J.
A1  - Kumar, N.
A1  - Smits, M.
A1  - van der Voort, S.R.
A1  - Alafandi, A.
A1  - Incekara, F.
A1  - Wijnenga, M.M.J.
A1  - Kapsas, G.
A1  - Gahrmann, R.
A1  - Schouten, J.W.
A1  - Dubbink, H.J.
A1  - Vincent, A.J.P.E.
A1  - van den Bent, M.J.
A1  - French, P.J.
A1  - Klein, S.
A1  - Yuan, Y.
A1  - Sharma, S.
A1  - Tseng, T.-C.
A1  - Adabi, S.
A1  - Niclou, S.P.
A1  - Keunen, O.
A1  - Hau, A.-C.
A1  - Vallières, M.
A1  - Fortin, D.
A1  - Lepage, M.
A1  - Landman, B.
A1  - Ramadass, K.
A1  - Xu, K.
A1  - Chotai, S.
A1  - Chambless, L.B.
A1  - Mistry, A.
A1  - Thompson, R.C.
A1  - Gusev, Y.
A1  - Bhuvaneshwar, K.
A1  - Sayah, A.
A1  - Bencheqroun, C.
A1  - Belouali, A.
A1  - Madhavan, S.
A1  - Booth, T.C.
A1  - Chelliah, A.
A1  - Modat, M.
A1  - Shuaib, H.
A1  - Dragos, C.
A1  - Abayazeed, A.
A1  - Kolodziej, K.
A1  - Hill, M.
A1  - Abbassy, A.
A1  - Gamal, S.
A1  - Mekhaimar, M.
A1  - Qayati, M.
A1  - Reyes, M.
A1  - Park, J.E.
A1  - Yun, J.
A1  - Kim, H.S.
A1  - Mahajan, A.
A1  - Muzi, M.
A1  - Benson, S.
A1  - Beets-Tan, R.G.H.
A1  - Teuwen, J.
A1  - Herrera-Trujillo, A.
A1  - Trujillo, M.
A1  - Escobar, W.
A1  - Abello, A.
A1  - Bernal, J.
A1  - Gómez, J.
A1  - Choi, J.
A1  - Baek, S.
A1  - Kim, Y.
A1  - Ismael, H.
A1  - Allen, B.
A1  - Buatti, J.M.
A1  - Kotrotsou, A.
A1  - Li, H.
A1  - Weiss, T.
A1  - Weller, M.
A1  - Bink, A.
A1  - Pouymayou, B.
A1  - Shaykh, H.F.
A1  - Saltz, J.
A1  - Prasanna, P.
A1  - Shrestha, S.
A1  - Mani, K.M.
A1  - Payne, D.
A1  - Kurc, T.
A1  - Pelaez, E.
A1  - Franco-Maldonado, H.
A1  - Loayza, F.
A1  - Quevedo, S.
A1  - Guevara, P.
A1  - Torche, E.
A1  - Mendoza, C.
A1  - Vera, F.
A1  - Ríos, E.
A1  - López, E.
A1  - Velastin, S.A.
A1  - Ogbole, G.
A1  - Soneye, M.
A1  - Oyekunle, D.
A1  - Odafe-Oyibotha, O.
A1  - Osobu, B.
A1  - Shu’aibu, M.
A1  - Dorcas, A.
A1  - Dako, F.
A1  - Simpson, A.L.
A1  - Hamghalam, M.
A1  - Peoples, J.J.
A1  - Hu, R.
A1  - Tran, A.
A1  - Cutler, D.
A1  - Moraes, F.Y.
A1  - Boss, M.A.
A1  - Gimpel, J.
A1  - Veettil, D.K.
A1  - Schmidt, K.
A1  - Bialecki, B.
A1  - Marella, S.
A1  - Price, C.
A1  - Cimino, L.
A1  - Apgar, C.
A1  - Shah, P.
A1  - Menze, B.
A1  - Barnholtz-Sloan, J.S.
A1  - Martin, J.
A1  - Bakas, S.
M1  - (Pati S.; Baid U.; Davatzikos C.; Sako C.; Bilello M.; Mohan S.; Shukla G.; Bakas S., [email protected]) Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, United States
M1  - (Pati S.; Baid U.; Davatzikos C.; Sako C.; Ghodasara S.; Bilello M.; Mohan S.; Dako F.; Bakas S., [email protected]) Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
M1  - (Pati S.; Baid U.; Bakas S., [email protected]) Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
M1  - (Pati S.; Ezhov I.; Menze B.) Department of Informatics, Technical University of Munich, Bavaria, Munich, Germany
M1  - (Edwards B.; Sheller M.; Wang S.-H.; Reina G.A.; Foley P.; Gruzdev A.; Karkada D.; Shah P.; Martin J.) Intel Corporation, Santa Clara, CA, United States
M1  - (Vollmuth P.; Brugnara G.; Preetha C.J.; Bendszus M.) Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
M1  - (Sahm F.; Wick W.) Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
M1  - (Sahm F.) Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
M1  - (Maier-Hein K.; Zenk M.) Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
M1  - (Maier-Hein K.) Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
M1  - (Wick W.) Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
M1  - (Calabrese E.; Rudie J.; Villanueva-Meyer J.; Cha S.) Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States
M1  - (Ingalhalikar M.; Jadhav M.; Pandey U.) Symbiosis Center for Medical Image Analysis, Symbiosis International University, Maharashtra, Pune, India
M1  - (Saini J.) Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Karnataka, Bangalore, India
M1  - (Garrett J.; Larson M.; Jeraj R.) Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States
M1  - (Garrett J.; Jeraj R.) Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States
M1  - (Currie S.; Frood R.; Fatania K.) Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, United Kingdom
M1  - (Huang R.Y.) Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
M1  - (Chang K.) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
M1  - (Balaña C.) Catalan Institute of Oncology, Badalona, Spain
M1  - (Capellades J.) Consorci MAR Parc de Salut de Barcelona, Catalonia, Spain
M1  - (Puig J.) Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
M1  - (Trenkler J.; Necker G.; Haunschmidt A.; Meckel S.) Institute of Neuroradiology, Kepler University Hospital Linz, Neuromed Campus (NMC), Linz, Austria
M1  - (Pichler J.) Department of Neurooncology, Kepler University Hospital Linz, Neuromed Campus (NMC), Linz, Austria
M1  - (Meckel S.) Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
M1  - (Shukla G.) Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, United States
M1  - (Liem S.; Lombardo J.) Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, United States
M1  - (Alexander G.S.) Department of Radiation Oncology, University of Maryland, Baltimore, MD, United States
M1  - (Lombardo J.; Dicker A.P.) Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, United States
M1  - (Palmer J.D.) Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, United States
M1  - (Flanders A.E.) Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, United States
M1  - (Sair H.I.) The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
M1  - (Sair H.I.; Jones C.K.) The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
M1  - (Venkataraman A.) Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
M1  - (Jiang M.; So T.Y.; Chen C.; Heng P.A.; Dou Q.) The Chinese University of Hong Kong, Hong Kong
M1  - (Kozubek M.; Lux F.; Michálek J.; Matula P.) Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
M1  - (Keřkovský M.; Kopřivová T.; Dostál M.) Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
M1  - (Dostál M.) Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
M1  - (Vybíhal V.) Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
M1  - (Vogelbaum M.A.) Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
M1  - (Mitchell J.R.; Kumar N.) University of Alberta, Edmonton, AB, Canada
M1  - (Mitchell J.R.; Verma R.; Kumar N.) Alberta Machine Intelligence Institute, Edmonton, AB, Canada
M1  - (Farinhas J.) Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
M1  - (Maldjian J.A.; Yogananda C.G.B.; Pinho M.C.; Reddy D.; Holcomb J.; Wagner B.C.) University of Texas Southwestern Medical Center, Dallas, TX, United States
M1  - (Ellingson B.M.; Raymond C.; Oughourlian T.) UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
M1  - (Ellingson B.M.; Cloughesy T.F.) UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
M1  - (Oughourlian T.; Hagiwara A.; Wang C.) Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
M1  - (To M.-S.; Bhardwaj S.) College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
M1  - (To M.-S.) Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
M1  - (Chong C.; Agzarian M.) South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
M1  - (Agzarian M.; Mandel J.) Department of Neurology, Baylor College of Medicine, Houston, TX, United States
M1  - (Falcão A.X.) Institute of Computing, University of Campinas, São Paulo, Campinas, Brazil
M1  - (Martins S.B.) Federal Institute of São Paulo, São Paulo, Campinas, Brazil
M1  - (Teixeira B.C.A.) Instituto de Neurologia de Curitiba, Paraná, Curitiba, Brazil
M1  - (Teixeira B.C.A.; Sprenger F.) Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
M1  - (Menotti D.; Lucio D.R.) Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
M1  - (LaMontagne P.; Marcus D.) Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
M1  - (Wiestler B.; Kofler F.; Metz M.) Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
M1  - (Wiestler B.; Kofler F.; Ezhov I.) TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
M1  - (Kofler F.; Ezhov I.) Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
M1  - (Jain R.; Lee M.; Lui Y.W.) Department of Radiology, NYU Grossman School of Medicine, New York, NY, United States
M1  - (Jain R.) Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, United States
M1  - (McKinley R.; Slotboom J.; Radojewski P.; Meier R.; Wiest R.) Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
M1  - (Murcia D.; Fu E.; Haas R.; Thompson J.; Ormond D.R.) Department of Neurosurgery, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States
M1  - (Badve C.) Department of Radiology, University Hospitals Cleveland, Cleveland, OH, United States
M1  - (Sloan A.E.) Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, United States
M1  - (Sloan A.E.) Case Comprehensive Cancer Center, Cleveland, OH, United States
M1  - (Sloan A.E.; Vadmal V.) Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, United States
M1  - (Waite K.; Barnholtz-Sloan J.S.) National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, United States
M1  - (Colen R.R.; Ak M.) Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, United States
M1  - (Colen R.R.) Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
M1  - (Pei L.) University of Pittsburgh Medical Center, Pittsburgh, PA, United States
M1  - (Srinivasan A.; Bapuraj J.R.; Yoshiaki O.; Moritani T.; Turk S.) Department of Neuroradiology, University of Michigan, Ann Arbor, MI, United States
M1  - (Rao A.; Wang N.; Lee J.; Prabhudesai S.) Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
M1  - (Morón F.) Department of Radiology, Baylor College of Medicine, Houston, TX, United States
M1  - (Kamnitsas K.; Glocker B.) Department of Computing, Imperial College London, London, United Kingdom
M1  - (Kamnitsas K.) Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
M1  - (Dixon L.V.M.) Department of Radiology, Imperial College NHS Healthcare Trust, London, United Kingdom
M1  - (Williams M.) Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, United Kingdom
M1  - (Zampakis P.; Kalogeropoulou C.) Department of NeuroRadiology, University of Patras, Patras, Greece
M1  - (Panagiotopoulos V.) Department of Neurosurgery, University of Patras, Patras, Greece
M1  - (Tsiganos P.) Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
M1  - (Alexiou S.; Zacharaki E.I.; Moustakas K.) Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
M1  - (Haliassos I.) Department of Neuro-Oncology, University of Patras, Patras, Greece
M1  - (Kardamakis D.M.) Department of Radiation Oncology, University of Patras, Patras, Greece
M1  - (Choi Y.S.; Lee S.-K.; Chang J.H.; Ahn S.S.) Yonsei University College of Medicine, Seoul, South Korea
M1  - (Luo B.; Wen N.) Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
M1  - (Poisson L.) Public Health Sciences, Henry Ford Health System, Detroit, MI, United States
M1  - (Wen N.) SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
M1  - (Tiwari P.; Verma R.; Bareja R.; Yadav I.; Chen J.) Case Western Reserve University, Cleveland, OH, United States
M1  - (Smits M.; van der Voort S.R.; Alafandi A.; Incekara F.; Kapsas G.; Gahrmann R.) Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
M1  - (Incekara F.; Schouten J.W.; Vincent A.J.P.E.) Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
M1  - (Wijnenga M.M.J.; van den Bent M.J.; French P.J.) Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
M1  - (Dubbink H.J.) Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
M1  - (Klein S.) Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
M1  - (Yuan Y.; Sharma S.; Tseng T.-C.; Adabi S.) Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
M1  - (Niclou S.P.; Hau A.-C.) NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
M1  - (Keunen O.) Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
M1  - (Hau A.-C.) Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
M1  - (Vallières M.) Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
M1  - (Vallières M.; Fortin D.; Lepage M.) Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
M1  - (Fortin D.) Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
M1  - (Lepage M.) Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
M1  - (Landman B.; Ramadass K.) Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
M1  - (Xu K.) Department of Computer Science, Vanderbilt University, Nashville, TN, United States
M1  - (Chotai S.; Chambless L.B.; Mistry A.; Thompson R.C.) Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, United States
M1  - (Gusev Y.; Bhuvaneshwar K.; Bencheqroun C.; Belouali A.; Madhavan S.) Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, United States
M1  - (Sayah A.) Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, United States
M1  - (Booth T.C.; Chelliah A.; Modat M.) School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
M1  - (Booth T.C.) Department of Neuroradiology, Ruskin Wing, King’s College Hospital NHS Foundation Trust, London, United Kingdom
M1  - (Shuaib H.; Dragos C.) Stoke Mandeville Hospital, Mandeville Road, Aylesbury, United Kingdom
M1  - (Shuaib H.; Simpson A.L.) Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada
M1  - (Abayazeed A.; Kolodziej K.; Hill M.) Neosoma Inc., Groton, MA, United States
M1  - (Abbassy A.; Gamal S.; Mekhaimar M.; Qayati M.) University of Cairo School of Medicine, Giza, Egypt
M1  - (Reyes M.) University of Bern, Bern, Switzerland
M1  - (Park J.E.; Yun J.; Kim H.S.) Department of Radiology, Asan Medical Center, Seoul, South Korea
M1  - (Mahajan A.) The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, United Kingdom
M1  - (Muzi M.) Department of Radiology, University of Washington, Seattle, WA, United States
M1  - (Benson S.; Teuwen J.) Netherlands Cancer Institute, Amsterdam, Netherlands
M1  - (Beets-Tan R.G.H.) Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
M1  - (Beets-Tan R.G.H.) GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
M1  - (Herrera-Trujillo A.; Escobar W.) Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
M1  - (Herrera-Trujillo A.; Trujillo M.; Escobar W.; Abello A.; Bernal J.; Gómez J.) Universidad del Valle, Cali, Colombia
M1  - (Bernal J.) The University of Edinburgh, Edinburgh, United Kingdom
M1  - (Choi J.) Department of Industrial and Systems Engineering, University of Iowa, IA, United States
M1  - (Baek S.) Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, United States
M1  - (Kim Y.; Ismael H.; Allen B.; Buatti J.M.) Department of Radiation Oncology, University of Iowa, Iowa City, IA, United States
M1  - (Kotrotsou A.) MD Anderson Cancer Center, University of Texas, Houston, TX, United States
M1  - (Li H.; Menze B.) Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
M1  - (Weiss T.; Weller M.) Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
M1  - (Bink A.; Pouymayou B.) Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
M1  - (Shaykh H.F.) University of Alabama in Birmingham, Birmingham, AL, United States
M1  - (Saltz J.; Prasanna P.; Shrestha S.; Mani K.M.; Kurc T.) Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
M1  - (Mani K.M.) Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, United States
M1  - (Payne D.) Department of Radiology, Stony Brook University, Stony Brook, NY, United States
M1  - (Kurc T.) Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, United States
M1  - (Pelaez E.; Loayza F.) Escuela Superior Politecnica del Litoral, Guayas, Guayaquil, Ecuador
M1  - (Franco-Maldonado H.) Sociedad de Lucha Contral el Cancer - SOLCA, Guayaquil Ecuador, Guayaquil, Ecuador
M1  - (Quevedo S.) Universidad Católica de Cuenca, Cuenca, Ecuador
M1  - (Guevara P.; Torche E.; Mendoza C.; Vera F.; Ríos E.; López E.) Universidad de Concepción, Biobío, Concepción, Chile
M1  - (Velastin S.A.) School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
M1  - (Ogbole G.; Soneye M.; Oyekunle D.; Osobu B.) Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
M1  - (Odafe-Oyibotha O.) Clinix Healthcare, Lagos, Lagos, Nigeria
M1  - (Shu’aibu M.) Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
M1  - (Dorcas A.) Department of Radiology, Obafemi Awolowo University Ile-Ife, Osun, Ile-Ife, Nigeria
M1  - (Dako F.) Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
M1  - (Simpson A.L.; Hamghalam M.; Peoples J.J.; Hu R.; Tran A.) School of Computing, Queen’s University, Kingston, ON, Canada
M1  - (Hamghalam M.) Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
M1  - (Cutler D.) The Faculty of Arts & Sciences, Queen’s University, Kingston, ON, Canada
M1  - (Moraes F.Y.) Department of Oncology, Queen’s University, Kingston, ON, Canada
M1  - (Boss M.A.; Gimpel J.; Veettil D.K.; Marella S.; Price C.; Cimino L.; Apgar C.) Center for Research and Innovation, American College of Radiology, Philadelphia, PA, United States
M1  - (Schmidt K.; Bialecki B.) Data Science Institute, American College of Radiology, Reston, VA, United States
M1  - (Barnholtz-Sloan J.S.) Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, United States
AD  - S. Bakas, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, United States
T1  - Author Correction: Federated learning enables big data for rare cancer boundary detection (Nature Communications, (2022), 13, 1, (7346), 10.1038/s41467-022-33407-5)
LA  - English
KW  - erratum
N2  - In this article the author name Carmen Balaña was incorrectly written as Carmen Balaña Quintero. The original article has been corrected.
ER  - 

TY  - JOUR
M3  - Article
Y1  - 2023
VL  - 6
IS  - 1
SN  - 2398-6352
JF  - npj Digital Medicine
JO  - npj Digit. Med.
UR  - https://www.embase.com/search/results?subaction=viewrecord&id=L2021345834&from=export
U2  - L2021345834
DB  - Embase
U3  - 2023-02-09
U4  - 2023-02-28
L2  - http://dx.doi.org/10.1038/s41746-023-00762-6
DO  - 10.1038/s41746-023-00762-6
LK  - https://slsp-unige.primo.exlibrisgroup.com/openurl/41SLSP_UGE/41SLSP_UGE:VU1?sid=EMBASE&sid=EMBASE&issn=23986352&id=doi:10.1038%2Fs41746-023-00762-6&atitle=A+tablet-based+game+for+the+assessment+of+visual+motor+skills+in+autistic+children&stitle=npj+Digit.+Med.&title=npj+Digital+Medicine&volume=6&issue=1&spage=&epage=&aulast=Perochon&aufirst=Sam&auinit=S.&aufull=Perochon+S.&coden=&isbn=&pages=-&date=2023&auinit1=S&auinitm=
A1  - Perochon, S.
A1  - Matias Di Martino, J.
A1  - Carpenter, K.L.H.
A1  - Compton, S.
A1  - Davis, N.
A1  - Espinosa, S.
A1  - Franz, L.
A1  - Rieder, A.D.
A1  - Sullivan, C.
A1  - Sapiro, G.
A1  - Dawson, G.
M1  - (Perochon S.; Matias Di Martino J.; Sapiro G., [email protected]) Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States
M1  - (Perochon S.) Ecole Normale Supérieure Paris-Saclay, Gif-Sur-Yvette, France
M1  - (Carpenter K.L.H.; Compton S.; Davis N.; Franz L.; Rieder A.D.; Sullivan C.; Dawson G., [email protected]) Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States
M1  - (Carpenter K.L.H.; Compton S.; Franz L.; Rieder A.D.; Sullivan C.; Dawson G., [email protected]) Duke Center for Autism and Brain Development, Duke University, Durham, NC, United States
M1  - (Espinosa S.) Office of Information Technology, Duke University, Durham, NC, United States
M1  - (Franz L.) Duke Global Health Institute, Duke University, Durham, NC, United States
AD  - G. Sapiro, Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States
AD  - G. Dawson, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States
T1  - A tablet-based game for the assessment of visual motor skills in autistic children
LA  - English
KW  - tablet computer
KW  - age
KW  - area under the curve
KW  - article
KW  - attention deficit hyperactivity disorder
KW  - autism
KW  - Autism Diagnostic Observation Schedule
KW  - child
KW  - clinical assessment
KW  - cognition
KW  - computer vision
KW  - controlled study
KW  - female
KW  - follow up
KW  - human
KW  - major clinical study
KW  - male
KW  - motor dysfunction
KW  - motor performance
KW  - Mullen scales of early learning
KW  - receiver operating characteristic
KW  - video game
N2  - Increasing evidence suggests that early motor impairments are a common feature of autism. Thus, scalable, quantitative methods for measuring motor behavior in young autistic children are needed. This work presents an engaging and scalable assessment of visual-motor abilities based on a bubble-popping game administered on a tablet. Participants are 233 children ranging from 1.5 to 10 years of age (147 neurotypical children and 86 children diagnosed with autism spectrum disorder [autistic], of which 32 are also diagnosed with co-occurring attention-deficit/hyperactivity disorder [autistic+ADHD]). Computer vision analyses are used to extract several game-based touch features, which are compared across autistic, autistic+ADHD, and neurotypical participants. Results show that younger (1.5-3 years) autistic children pop the bubbles at a lower rate, and their ability to touch the bubble’s center is less accurate compared to neurotypical children. When they pop a bubble, their finger lingers for a longer period, and they show more variability in their performance. In older children (3-10-years), consistent with previous research, the presence of co-occurring ADHD is associated with greater motor impairment, reflected in lower accuracy and more variable performance. Several motor features are correlated with standardized assessments of fine motor and cognitive abilities, as evaluated by an independent clinical assessment. These results highlight the potential of touch-based games as an efficient and scalable approach for assessing children’s visual-motor skills, which can be part of a broader screening tool for identifying early signs associated with autism.
ER  - 

sample_rayyan.ris

TY  - JOUR
AN  - rayyan-536206336
TI  - Organic fertilization and alternative products in the control of powdery mildew
Y1  - 2020
Y2  - 3
T2  - Ornamental Horticulture
SN  - 2447-536X
J2  - Ornamental Horticulture
VL  - 26
IS  - 1
SP  - 57-68
AU  - Ramos, Sabrina Maiháve Barbosa
AU  - Almeida, Elka Fabiana Aparecida
AU  - Rocha, Fernando da Silva
AU  - Fernandes, Maria de Fátima Gonçalves
AU  - Santos, Ellen Beatriz dos
UR  - http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2447-536X2020000100057&lang=en
LA  - en
KW  - Rosa sp. Oidium leucoconium
KW  - severidade
KW  - bicarbonato de sódio
KW  - ácido pirolenhoso de café
KW  - severity
KW  - sodium bicarbonate
KW  - coffee pyroligneous acid
KW  - Self-Fertilization
AB  - Abstract Rose is a plant of high nutritional requirement, susceptible to powdery mildew disease caused by fungus Oidium leucoconium, which causes leaf fall and losses in flower production. The objective of this study was to evaluate powdery mildew severity in rose cultivar ‘Grand Gala’ in response to organic fertilization and the application of alternative products to disease control. The first experiment was set in a factorial arrangement, with 5 alternative products: spraying with water as a control (PA), lime sulfur (CS), neem oil (ON), mixture of sodium bicarbonate and canola oil (BC) and coffee pyroligneous acid (APC) and 2 organic fertilizers: chicken manure (EA) and biofertilizer based on banana stalk (B). Disease severity was assessed at 0, 15, 30 and 45 days after the treatments. In the second experiment, asymptomatic leaves or with different powdery mildew severity levels were sprayed only once with the same alternative products mentioned above. Severity was assessed at 0, 7 and 14 days. The organic fertilizations did not influence the reduction in powdery mildew severity in rose. At 45 days, APC yielded a greater reduction in disease severity (81.6%), followed by treatments based on BC, ON and CS. Greater reduction in disease severity in experiment 2 occurred in the treatments of BC and CS, followed by APC. Therefore, it is possible to conclude that APC and the BC have the potential to control rose powdery mildew in an organic cultivation system.
AB  - Resumo A roseira é uma planta de elevada exigência nutricional e suscetível ao oídio, doença que causa queda de folhas e perdas na produção de flores. Objetivou-se com esse trabalho avaliar a severidade de oídio em roseira cultivar ‘Grand Gala’ em resposta à adubação orgânica e a aplicação de produtos alternativos para o controle da doença. O primeiro experimento foi instalado em arranjo fatorial, sendo 5 produtos alternativos: água potável como testemunha- (AP), calda sulfocálcica (CS); óleo de nem (ON), mistura de bicarbonato de sódio mais óleo de canola (BC), ácido pirolenhoso de café (APC) e 2 adubações orgânicas: esterco de aves (EA) e biofertilizante à base de engaço de bananeira (B). As avaliações da severidade da doença foram realizadas aos 0, 15, 30 e 45 dias após os tratamentos. No segundo experimento, folhas assintomáticas ou com diferentes severidades de oídio foram pulverizadas apenas uma vez com os mesmos produtos alternativos mencionados anteriormente. A avaliação da severidade foi feita aos 0, 7 e 14 dias. As adubações orgânicas não influenciaram na redução da severidade do oídio da roseira. Aos 45 dias, APC proporcionou maior redução na severidade da doença (81,6%), seguido pelos tratamentos à base BC (61%), ON (58,96%) e CS (60,3%). Maior redução da severidade da doença no experimento 2 ocorreu nos tratamentos com BC e CS, seguida pelo APC. Portanto, pode-se concluir que o APC e BC possuem potencial no controle do oídio da roseira em sistema de cultivo orgânico.
DO  - 10.1590/2447-536x.v26i1.2109
ER  -

TY  - JOUR
AN  - rayyan-536206337
TI  - CARBON BALANCE IN ORGANIC CONILON COFFEE INTERCROPPED WITH TREE SPECIES AND BANANA
Y1  - 2020
T2  - Revista Árvore
SN  - 0100-6762
J2  - Revista Árvore
VL  - 44
SP  - -
AU  - Silva, Diego Mathias Natal da
AU  - Heitor, Letícia Célia
AU  - Candido, Aildosn de Oliveira
AU  - Moraes, Bárbara Santos Antônio de
AU  - Souza, Gustavo Soares de
AU  - Araújo, João Batista Silva
AU  - Mendonça, Eduardo de Sá
UR  - http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622020000100219&lang=en
LA  - en
KW  - Coffea canephora
KW  - Agroforestry system
KW  - Soil organic matter
KW  - Musa
KW  - Trees
KW  - Coffee
AB  - ABSTRACT Over the last decade, conilon coffee (Coffea canephora) in consortium with wood trees has been established to improve environmental conditions. Little is known about how individual wood trees and banana affect soil quality when intercropped with conilon coffee. The objective of the present study was to evaluate the impacts of intercropping organic conilon coffee with different wood tree species and banana on C balance. Five cultivation systems including conilon coffee monoculture and intercropped with Inga edulis, Gliricidia sepium, BRS Japira banana (Musa sp.), or Bactris gasipaes were studied in a randomized complete block design, with four replicates at the south of Espírito Santo State, Brazil. A primary forest fragment adjacent to the experiment was also evaluated for comparison with the consortium. Samples of topsoil (0 to 10 cm) were collected in 2016 to evaluate the total organic C and total N. Soil temperature and moisture at 0 to 5 cm depth and the CO2 emission were measured monthly over one year. The species planted with the conilon coffee promoted a 5.52% decrease in the soil temperature and a 17% increase in the soil moisture content. They also promoted an increase in annual C balance, especially intercropped with Gliricidia and Inga (4.70 and 3.56 Mg ha-1, respectively), with a substantial increase in the soil total organic C and total N in both systems.
AB  - RESUMO Na última década, o café conilon (Coffea canephora) em consórcio com árvores foi estabelecido para melhorar as condições ambientais. Pouco se sabe sobre como o cultivo de árvores e de banana afetam a qualidade do solo quando consorciados com café conilon. O objetivo do presente estudo foi avaliar os impactos do consórcio de café conilon orgânico com diferentes espécies arbóreas e banana no balanço de C. Cinco sistemas de cultivo, incluindo monocultura de café conilon e consorciados com Inga edulis, Gliricidia sepium, banana BRS Japira (Musa sp.) ou Bactris gasipaes foram estudados em delineamento de blocos ao acaso, com quatro repetições no sul do Espírito Santo, Brasil. Um fragmento de floresta primária adjacente ao experimento foi avaliado para comparação com o consórcio. Amostras superficiais de solo (0 a 10 cm) foram coletadas em 2016 para avaliar o C orgânico total e o N total. A temperatura e umidade do solo de 0 a 5 cm de profundidade e a emissão de CO2 foram medidas mensalmente ao longo de um ano. As espécies plantadas com o café conilon promoveram redução de 5,52% na temperatura do solo e aumento de 17% no teor de umidade do solo. Também promoveu aumento no balanço anual de C, consorciado especialmente com Gliricidia e Inga (4,70 e 3,56 Mg ha-1, respectivamente), com aumento substancial no C orgânico total do solo e N total nos dois sistemas.
DO  - 10.1590/1806-908820200000021
ER  -

TY  - JOUR
AN  - rayyan-536206338
TI  - Efeitos de atributos ambientais na biodiversidade de formigas sob diferentes usos do solo
Y1  - 2019
Y2  - 6
T2  - Ciência Florestal
SN  - 1980-5098
J2  - Ciência Florestal
VL  - 29
IS  - 2
SP  - 660-672
AU  - Amaral, Gustavo Correiro do
AU  - Vargas, André Barbosa
AU  - Almeida, Fábio Souto
UR  - http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1980-50982019000200660&lang=en
LA  - pt
KW  - Conservação
KW  - Formicidae
KW  - Sistemas agroflorestais
KW  - Agroforestry systems
KW  - Conservation
AB  - Abstract The objective was to evaluate the influence of environmental variables on the ant fauna, in cultivated areas and in a native forest fragment. The data were collected in the municipality of Seropédica, Rio de Janeiro state, in the rainy and dry seasons, in a banana plantation (Musa pariadisiaca L.), an agroforest, a coffee plantation (Coffea conephora Pierre) shaded with Gliricidia sepium (Jacq.) and a forest fragment (8 ha) in the intermediate stage of secondary succession. For the sampling, soil pitfall traps and the Winkler extractor were used. A total of 53 species were collected in the present study. The highest species richness was observed in the agroforestry (38 species), followed by shaded coffee (31 species), secondary forest (29 species) and banana plantation (28 species). In the dry season there was effect of land use type and leaf litter depth on the richness and diversity of ant species. In both seasons, ant species composition varied significantly among all the studied areas. The agroforestry systems are important alternatives to monocultures due to their diversity of cultivated species and plant strata. Thus, the increase of the structural heterogeneity of the environment and the leaf litter depth, in cultivated areas in the dry season, may lead to greater species richness and diversity as well as changes in the species composition.
AB  - Resumo O objetivo foi avaliar a influência de variáveis ambientais sobre a fauna de formigas em um fragmento de floresta nativa e em áreas cultivadas. Os dados foram coletados no município de Seropédica - RJ, nas estações chuvosa e seca, em um cultivo de bananas (Musa pariadisiaca L.), uma agrofloresta, um cafezal (Coffea conephora Pierre) sombreado com Gliricidia sepium (Jacq.) e um fragmento de floresta (8 ha) em estágio intermediário de sucessão secundária. Para a amostragem, foram utilizadas armadilhas de queda tipo pitfall e extratores de Winkler. Foram coletadas 53 espécies de formigas, ocorrendo maior riqueza de espécies na agrofloresta (38 espécies), seguida do cafezal (31 espécies), floresta secundária (29 espécies) e plantação de bananeiras (28 espécies). Na estação seca houve o efeito do tipo de uso do solo e da profundidade de serapilheira sobre a riqueza e diversidade de espécies de formigas. Nas duas estações, a composição de espécies de formigas variou significativamente entre todas as áreas estudadas. Os sistemas agroflorestais funcionam como alternativa às monoculturas pela diversidade de espécies cultivadas e estratos vegetais que apresentam. Neste sentido, o aumento da heterogeneidade estrutural do ambiente e da profundidade de serapilheira, em áreas cultivadas na estação seca, proporciona maior riqueza e diversidade de espécies de formigas, além de mudanças na composição de espécies.
DO  - 10.5902/1980509833811
ER  -

sample_wos.ris

TY  - JOUR
AU  - Thevenot, J
AU  - Lopez, MB
AU  - Hadid, A
TI  - A Survey on Computer Vision for Assistive Medical Diagnosis From Faces
T2  - IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
LA  - English
KW  - Computer vision
KW  - face analysis
KW  - facial symptoms
KW  - imaging
KW  - medical diagnosis
KW  - FETAL ALCOHOL SYNDROME
KW  - LOCAL BINARY PATTERNS
KW  - INFRARED THERMOGRAPHY
KW  - FACIAL PHENOTYPE
KW  - QUANTITATIVE-ANALYSIS
KW  - MORPHOMETRIC-ANALYSIS
KW  - MASTICATORY MUSCLES
KW  - AIDED DIAGNOSIS
KW  - PAIN EXPRESSION
KW  - MOTION ANALYSIS
AB  - Automatic medical diagnosis is an emerging center of interest in computer vision as it provides unobtrusive objective information on a patient's condition. The face, as a mirror of health status, can reveal symptomatic indications of specific diseases. Thus, the detection of facial abnormalities or atypical features is at up most importance when it comes to medical diagnostics. This survey aims to give an overview of the recent developments in medical diagnostics from facial images based on computer vision methods. Various approaches have been considered to assess facial symptoms and to eventually provide further help to the practitioners. However, the developed tools are still seldom used in clinical practice, since their reliability is still a concern due to the lack of clinical validation of the methodologies and their inadequate applicability. Nonetheless, efforts are being made to provide robust solutions suitable for healthcare environments, by dealing with practical issues such as real-time assessment or patients positioning. This survey provides an updated collection of the most relevant and innovative solutions in facial images analysis. The findings show that with the help of computer vision methods, over 30 medical conditions can be preliminarily diagnosed from the automatic detection of some of their symptoms. Furthermore, future perspectives, such as the need for interdisciplinary collaboration and collecting publicly available databases, are highlighted.
AD  - Univ Oulu, Med Imaging Phys & Technol Res Unit, Oulu 90014, Finland
AD  - Univ Oulu, Ctr Machine Vis & Signal Anal, Oulu 90014, Finland
C3  - University of Oulu
C3  - University of Oulu
PU  - IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI  - PISCATAWAY
PA  - 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN  - 2168-2194
SN  - 2168-2208
J9  - IEEE J BIOMED HEALTH
JI  - IEEE J. Biomed. Health Inform.
DA  - SEP
PY  - 2018
VL  - 22
IS  - 5
SP  - 1497
EP  - 1511
DO  - 10.1109/JBHI.2017.2754861
WE  - Science Citation Index Expanded (SCI-EXPANDED)
WE  - Social Science Citation Index (SSCI)
AN  - WOS:000441795800017
N1  - Times Cited in Web of Science Core Collection:  70
Total Times Cited:  71
Cited Reference Count:  196
ER  -

TY  - JOUR
AU  - Parvaiz, A
AU  - Khalid, MA
AU  - Zafar, R
AU  - Ameer, H
AU  - Ali, M
AU  - Fraz, MM
TI  - Vision Transformers in medical computer vision-A contemplative retrospection
T2  - ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
LA  - English
KW  - Vision Transformers
KW  - Medical image analysis
KW  - Self attention
KW  - Medical computer vision
KW  - Diagnostic image analysis
KW  - Literature survey
KW  - CONVOLUTIONAL NEURAL-NETWORK
KW  - BARRETTS-ESOPHAGUS
KW  - IMAGE DATABASE
KW  - LUNG-CANCER
KW  - SEGMENTATION
KW  - TOMOGRAPHY
KW  - COVID-19
KW  - CNN
KW  - LOCALIZATION
KW  - NODULES
AB  - Vision Transformers (ViTs), with the magnificent potential to unravel the information contained within images, have evolved as one of the most contemporary and dominant architectures that are being used in the field of computer vision. These are immensely utilized by plenty of researchers to perform new as well as former experiments. Here, in this article, we investigate the intersection of vision transformers and medical images. We proffered an overview of various ViT based frameworks that are being used by different researchers to decipher the obstacles in medical computer vision. We surveyed the applications of Vision Transformers in different areas of medical computer vision such as image-based disease classification, anatomical structure segmentation, registration, region-based lesion detection, captioning, report generation, and reconstruction using multiple medical imaging modalities that greatly assist in medical diagnosis and hence treatment process. Along with this, we also demystify several imaging modalities used in medical computer vision. Moreover, to get more insight and deeper understanding, the self-attention mechanism of transformers is also explained briefly. Conclusively, the ViT based solutions for each image analytics task are critically analyzed, open challenges are discussed and the pointers to possible solutions for future direction are deliberated. We hope this review article will open future research directions for medical computer vision researchers.
AD  - Natl Univ Sci & Technol NUST, Islamabad 44000, Pakistan
C3  - National University of Sciences & Technology - Pakistan
PU  - PERGAMON-ELSEVIER SCIENCE LTD
PI  - OXFORD
PA  - THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN  - 0952-1976
SN  - 1873-6769
J9  - ENG APPL ARTIF INTEL
JI  - Eng. Appl. Artif. Intell.
DA  - JUN
PY  - 2023
VL  - 122
C7  - 106126
DO  - 10.1016/j.engappai.2023.106126
C6  - MAR 2023
WE  - Science Citation Index Expanded (SCI-EXPANDED)
AN  - WOS:000959475400001
N1  - Times Cited in Web of Science Core Collection:  3
Total Times Cited:  3
Cited Reference Count:  243
ER  -

TY  - JOUR
AU  - Seo, J
AU  - Han, S
AU  - Lee, S
AU  - Kim, H
TI  - Computer vision techniques for construction safety and health monitoring
T2  - ADVANCED ENGINEERING INFORMATICS
LA  - English
KW  - Construction safety and health
KW  - Computer vision
KW  - Monitoring
KW  - TRACKING METHODS
KW  - IMAGE
KW  - EQUIPMENT
KW  - RECOGNITION
KW  - MODEL
KW  - CLASSIFICATION
KW  - IDENTIFICATION
KW  - SEGMENTATION
KW  - WORKPLACE
KW  - CAPTURE
AB  - For construction safety and health, continuous monitoring of unsafe conditions and action is essential in order to eliminate potential hazards in a timely manner. As a robust and automated means of field observation, computer vision techniques have been applied for the extraction of safety related information from site images and videos, and regarded as effective solutions complementary to current time-consuming and unreliable manual observational practices. Although some research efforts have been directed toward computer vision-based safety and health monitoring, its application in real practice remains premature due to a number of technical issues and research challenges in terms of reliability, accuracy, and applicability. This paper thus reviews previous attempts in construction applications from both technical and practical perspectives in order to understand the current status of computer vision techniques, which in turn suggests the direction of future research in the field of computer vision-based safety and health monitoring. Specifically, this paper categorizes previous studies into three groups-object detection, object tracking, and action recognition-based on types of information required to evaluate unsafe conditions and acts. The results demonstrate that major research challenges include comprehensive scene understanding, varying tracking accuracy by camera position, and action recognition of multiple equipment and workers. In addition, we identified several practical issues including a lack of task-specific and quantifiable metrics to evaluate the extracted information in safety context, technical obstacles due to dynamic conditions at construction sites and privacy issues. These challenges indicate a need for further research in these areas. Accordingly, this paper provides researchers insights into advancing knowledge and techniques for computer vision-based safety and health monitoring, and offers fresh opportunities and considerations to practitioners in understanding and adopting the techniques. (C) 2015 Elsevier Ltd. All rights reserved.
AD  - Univ Michigan, Dept Civil & Environm Engn, Tishman Construct Management Program, Ann Arbor, MI 48109 USA
AD  - Univ Alberta, Dept Civil & Environm Engn, Edmonton, AB T6G 2W2, Canada
AD  - Yonsei Univ, Sch Civil & Environm Engn, Seoul 120749, South Korea
C3  - University of Michigan System
C3  - University of Michigan
C3  - University of Alberta
C3  - Yonsei University
FU  - National Science Foundation [CMMI-1161123]; Directorate For Engineering [1161123] Funding Source: National Science Foundation; Div Of Civil, Mechanical, & Manufact Inn [1161123] Funding Source: National Science Foundation
FX  - The work presented in this paper was supported financially with a National Science Foundation Award (No. CMMI-1161123). Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.
PU  - ELSEVIER SCI LTD
PI  - OXFORD
PA  - THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN  - 1474-0346
SN  - 1873-5320
J9  - ADV ENG INFORM
JI  - Adv. Eng. Inform.
DA  - APR
PY  - 2015
VL  - 29
IS  - 2
SP  - 239
EP  - 251
DO  - 10.1016/j.aei.2015.02.001
WE  - Science Citation Index Expanded (SCI-EXPANDED)
WE  - Social Science Citation Index (SSCI)
AN  - WOS:000355038200009
N1  - Times Cited in Web of Science Core Collection:  266
Total Times Cited:  272
Cited Reference Count:  93
ER  -