Výsledky vyhledávání - "Nyboe Ørting, Silas"

Feature learning based on visual similarity triplets in medical image analysis

Autor: Nyboe Ørting, Silas, Petersen, Jens, Cheplygina, Veronika, Thomsen, Laura H., Wille, Mathilde M.W., de Bruijne, Marleen, Lee, Su-Lin, Trucco, Emanuele, Maier-Hein, Lena, Moriconi, Stefano, Albarqouni, Shadi, Jannin, Pierre, Balocco, Simone, Zahnd, Guillaume, Mateus, Diana, Taylor, Zeike, Demirci, Stefanie, Stoyanov, Danail, Sznitman, Raphael, Martel, Anne, Granger, Eric, Duong, Luc

Publikováno v: Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis ISBN: 9783030013639
CVII-STENT/LABELS@MICCAI
Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis-7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018 Held in Conjunction with MICCAI 2018, 140-149
STARTPAGE=140;ENDPAGE=149;TITLE=Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis-7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018 Held in Conjunction with MICCAI 2018

Supervised feature learning using convolutional neural networks (CNNs) can provide concise and disease relevant representations of medical images. However, training CNNs requires annotated image data. Annotating medical images can be a time-consuming

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