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 Results 1 - 15 of 15  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
MI 2020-01-29
Okinawa OKINAWAKEN SEINENKAIKAN [Short Paper] Growth process analysis of nodular shadows in chest CT images using function expression
Shoko Inagaki, Takeshi Hara, Xiangrong Zhou (Gifu Univ.), Taiki Nozaki, Masaki Matsusako (St. Luke's Hospital) MI2019-79
Volume changes of lung nodules were recognized to prognose and to predict the abnormalities in the follow-up treatments.... [more] MI2019-79
PRMU 2019-10-19
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
SIP, MI, IE 2019-05-24
Aichi   Analysis of deaeration deformation for ex vivo animal lung
Kotaro Kobayashi, Megumi Nakao, Junko Tokuno, Toyofumi F. Chen-Yoshikawa, Hiroshi Date, Tetsuya Matsuda (Kyoto Univ.) SIP2019-12 IE2019-12 MI2019-12
Recent advance of imaging techniques enables to visualize minute lung nodules in the early stage cancer. Although lung n... [more] SIP2019-12 IE2019-12 MI2019-12
MI 2019-01-22
Okinawa   Segmentation of lung nodules on 3D CT images by using DeconvNet and V-Net
Shunsuke Kidera, Shoji Kido, Yasushi Hirano (Yamaguchi Univ.), Nobuyuki Tanaka (Saiseikai Hosp) MI2018-85
Semantic segmentation of lung nodules is important for texture analysis. However, manual segmentation needs a lot of tim... [more] MI2018-85
MI 2019-01-23
Okinawa   [Short Paper] A Lung Cancer Risk Prediction Model based on Clinical Information and Chest CT Images Analyses
Takeru Kageyama, Yoshiki Kawata, Noboru Niki (Tokushima Univ.), Masahiko Kusumoto (National Cancer Center), Hironobu Ohmatsu (Abashiri Prison), Yoshiki Aokage, Takaaki Tsuchida, Yuji Matsumoto (National Cancer Center), Kenji Eguchi (Teikyo Univ.), Masahiro Kaneko (Tokyo Health Service Association Health Support Center) MI2018-98
Lung cancer accounts for the number of cancer deaths first, and it is on an increasing trend. Although lung cancer CT sc... [more] MI2018-98
MI 2018-07-24
Iwate aiina (Morioka, Iwate) Improving Accuracy of Lung nodules Detection by Transfer Learning
Tatsuya Yamazaki, Hayato Yamakawa, Norihiko Yoshimura, Motohiko Yamazaki (Niigata Univ.) MI2018-29
In Japan, cancer is the first leading cause of death and, in particular, fatalities of lung cancer is increasing recentl... [more] MI2018-29
MI 2015-03-02
Okinawa Hotel Miyahira Image Feature Extraction and Construction of a Classifier for Discriminating Pulmonary Nodules in X-CT Images
Ryuta Mori, Takumi Naito, Hidekata Hontani (NIT), Shingo Iwano (Nagoya Univ.) MI2014-61
In this article, the authors report about a method for classifying pulmonary nodules in three-dimensional X-CT images an... [more] MI2014-61
MI 2012-01-19
Okinawa   [Special Talk] Computer-aided differential diagnosis of lung cancer using three-dimensional thoracic CT images
Yoshiki Kawata, Noboru Niki (Tokushima Univ) MI2011-108
In the recent release of positive results from the National Lung Screening Trial (NLST) screening trial in the US showin... [more] MI2011-108
MI 2011-07-12
Hokkaido Hokkaido University Investigation of fundamental techniques for autonomous computer-aided diagnosis system
Yongbum Lee, Du-Yih Tsai, Yuriko Yoshida (Niigata Univ.) MI2011-33
A novel application of computer-aided diagnosis (CAD), namely autonomous CAD, was proposed in this study. Autonomous CAD... [more] MI2011-33
MI 2011-01-20
Okinawa Naha-Bunka-Tembusu [Poster Presentation] Detection of Lung Nodules in Chest Radiographs based on Hessian Filter Bank
Tatsuya Nakamura, Yoshikazu Uchiyama (Oita NCT), Takeshi Hara, Hiroshi Fujita (Gifu Univ) MI2010-100
Radiologists can fail to detect approximately 30% of lung nodules on chest radiograph. Therefore, in order to assist rad... [more] MI2010-100
MI 2009-09-16
Fukuoka Kyushu Institute of Technology Library A Contralateral Subtraction Scheme for Detection of Pulmonary Nodules in Chest Radiographs
Yoshitomi Harada, Ryoichi Nagata, Tsuyoshi Kawaguchi, Hidetoshi Miyake (Oita Univ.) MI2009-55
Contralateral subtraction (C-sub) is a computer-aided diagnosis technique for detecting pulmonary nodules in chest radio... [more] MI2009-55
MI 2009-01-19
Overseas National Taiwan University Automatic Lung Nodule Registration in Serial CT Scans
Hyun Hee Jo, Helen Hong (Seoul Women's Univ.), Jin Mo Goo, Chang Min Park (Seoul National Univ. Hospital) MI2008-69
To identify corresponding pulmonary nodules in serial CT scans for interval change analysis, we propose a multi-stage re... [more] MI2008-69
MI 2009-01-19
Overseas National Taiwan University [Poster Presentation] High-speed Detection Method of Solitary Nodules in 3D Chest CT images Based on Cylindrical Filter
Atsushi Teramoto (Fujita Health Univ.), Masatoshi Tsuzaka (Nagoya Univ.), Takeshi Hara, Hiroshi Fujita (Gifu Univ.) MI2008-77
A number of researchers have developed two-dimensional (2D) and three dimensional (3D) methods for detecting solitary no... [more] MI2008-77
MI 2009-01-20
Overseas National Taiwan University Classification of Benign/Malignant PNGGOs using K-means algorithm in MDCT Images: A Preliminary Study
Wooram Son, Sang Joon Park, Chang Min Park, Jong Hyo Kim (Seoul National Univ.) MI2008-115
Lung cancer is one of the most prevalent diseases in the world. Recently, PNGGOs (Pure nodular ground-glass opacity) hav... [more] MI2008-115
MBE 2005-07-29
Tokushima   The Impact of Postprocessing Techniques on the Interpretation of MDCT data in Patients with Lung Nodules
Kazuhide Yoneda, Junji Ueno, Tetsuya Tujikawa, Kaori Furutani, Naomi Morita, Hideki Ohtsuka, Hiromu Nishitani (Tokushima Univ.)
The purpose of this study is to determine if combination of adequate postprocessing techniques and thin section image da... [more] MBE2005-24
 Results 1 - 15 of 15  /   
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