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Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU 2019-10-19
10:45
Tokyo   Localization of Diffuse Lung Deseases' Lesions and Quantification of Their Volumes Using Deep Learning
Hiroaki Takebe, Yasutaka Moriwaki, Nobuhiro Miyazaki, Takayuki Baba (FUJITSU LAB.), Hiroaki Terada, Toru Higaki, Kazuo Awai (Hiroshima Univ.), Hirotaka Kobayashi, Machiko Nakagawa, Masahiko Shimada, Kenji Kitayama (FUJITSU) PRMU2019-44
Changes in the amount of lesion over time are important information for diagnostic imaging of diffuse lung disease in wh... [more] PRMU2019-44
pp.67-72
NC, MBE
(Joint)
2019-03-06
16:40
Tokyo University of Electro Communications Study on data augmentation using Fourier transform for texture image classification
Daigo Nitta, Hayaru Shouno (UEC) NC2018-87
In the field of medical imaging such like computed tomography analysis, it is difficult to prepare a sufficient amount o... [more] NC2018-87
pp.233-238
MI 2010-01-29
09:40
Okinawa Naha-Bunka-Tenbusu The effect of training with simple pointed lesion dataset on classification accuracy -- Preliminary study for development of CAD software with incremental learning function on clinical application --
Mitsutaka Nemoto, Yukihiro Nomura (Univ. Tokyo Hospital), Yoshitaka Masutani (Univ. Tokyo Hospital/Univ. Tokyo), Shouhei Hanaoka (Univ. Tokyo), Takeharu Yoshikawa, Naoto Hayashi, Naoki Yoshioka (Univ. Tokyo Hospital), Kuni Ohtomo (Univ. Tokyo Hospital/Univ. Tokyo) MI2009-121
CAD (Computer Assisted Detection) software in clinical use needs a robustness for image feature variations caused by dif... [more] MI2009-121
pp.247-252
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