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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 30  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
MI 2024-03-04
10:10
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Segmentation of lung nodules in CT using an ensemble of multiple MTANN
Kentaro Someya, Shogo Kodera, Hiroko Oshibe, Jin Ze, Kenji Suzuki (Tokyo Tech) MI2023-67
It is challenging to accurately and robustly segment ground-glass opacity (GGO) nodules. In this study, we propose an ac... [more] MI2023-67
pp.113-116
MI 2021-03-16
13:30
Online Online Effect of Improving Versatility of Lung Nodules Classification Model by Fine-Tuning
Taku Ri, Tatsuya Yamazaki (Niigata Univ.) MI2020-72
In order to improve the survival rate of lung cancer patients, it is important to detect nodules at an early stage, but ... [more] MI2020-72
pp.102-107
MI 2020-01-29
13:20
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
pp.67-69
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
MI 2019-01-22
15:35
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
pp.103-106
MI 2019-01-23
14:00
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
pp.161-163
MI 2018-07-24
15:05
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
pp.39-43
MI 2015-03-02
10:54
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
pp.45-48
MI 2014-01-26
13:30
Okinawa Bunka Tenbusu Kan Computerized scheme for determination of the likelihood of malignancy of pulmonary nodules on HRCT and PET scans
Yuichiro Takaki, Masahito Aoyama (Hiroshima City Univ.), Daisuke Komoto, Toru Higaki (Hiroshima Univ.), Shinsaku Hiura (Hiroshima City Univ.), Kazuo Awai (Hiroshima Univ.) MI2013-77
We evaluated the performance of our automated computerized scheme for determining the likelihood of malignancy of pulmon... [more] MI2013-77
pp.115-120
R 2013-11-14
14:50
Osaka   Effect of Plate-Shape Ni-Sn IMC on the Growth Mechanism of Tin Whisker under Thermal Shock Stress
Akira Saito, Akira Okamoto, Yoshihiro Iwahori, Makoto Ogawa (Mutrata MFG), Akihiro Motoki (Sabae Mutrata MFG) R2013-76
With the promotion of lead-free solder in these years, some segments have expressed concern about the possibility of tin... [more] R2013-76
pp.11-16
PRMU 2013-03-15
10:45
Tokyo   Performance Comparison of Appearance-based Pulmonary Nodule Detection from X-ray CT Images
Takanobu Yanagihara, Hotaka Takizawa (Univ. of Tsukuba) PRMU2012-212
In this paper, we propose appearance-based clustering methods of false positives in X-ray CT images based on K-means, Me... [more] PRMU2012-212
pp.193-197
MI 2013-01-25
11:30
Okinawa Bunka Tenbusu Kan [Special Talk] Development of "SYNAPSE Case Match", content-based image retrieval system for supporting lung Cancer diagnosis
Akira Oosawa (Fujifilm) MI2012-101
We developed “SYNAPSE Case Match”, content-based image retrieval system for supporting Lung Cancer diagnosis, in collabo... [more] MI2012-101
pp.207-209
MI 2013-01-25
13:10
Okinawa Bunka Tenbusu Kan A pilot study of lung voxel classification for auto-detecting ground glass opacity nodules in chest CT images
Mitsutaka Nemoto, Yoshitaka Masutani, Shouhei Hanaoka, Yukihiro Nomura, Soichiro Miki, Takeharu Yoshikawa, Naoto Hayashi, Kuni Ohtomo (Univ. Tokyo) MI2012-109
In this study, we examine various voxel classification methods for auto-extracting ground glass opacity (GGO) nodule can... [more] MI2012-109
pp.245-248
MI 2012-01-19
11:20
Okinawa   Automatic segmentation of pulmonary nodules on CT images considering visual characteristics
Hideki Tamura, Rie Tachibana (Oshima NCMT), Yasushi Hirano, Rui Xu, Shoji Kido (Yamaguchi Univ.) MI2011-106
Radiologists recognize pulmonary nodules on CT images with graphical information from the visual sense. So, in this pape... [more] MI2011-106
pp.159-163
MI 2012-01-20
09:30
Okinawa   Evaluation of Temporal Subtraction Images of Chest Radiographs by Using Pixel Matching Technique
Aiko Sugimoto (Kumamoto Univ.), Noritaka Higashi (Japan Red Cross Kumamoto Personnel Health Manage), Yoshikazu Uchiyama, Shigehiko Katsuragawa, Junji Shiraishi (Kumamoto Univ.) MI2011-117
We have developed a pixel matching technique in order to reduce artifact in temporal subtraction images of sequential ch... [more] MI2011-117
pp.221-225
MI 2012-01-20
09:45
Okinawa   Pulmonary Nodule Detection from X-ray CT images using Moment-of-Inertia Filter with Radial Suppression Filter
Takanobu Yanagihara, Hotaka Takizawa (Univ. of Tsukuba) MI2011-118
In this report, we propose a detection method of pulmonary nodules in X-ray CT images using several image filters. First... [more] MI2011-118
pp.227-230
MI 2011-09-06
13:55
Ibaraki AIST Pulmonary Nodule Detection from X-ray CT Images Using Discriminant Filters and K-means Clustering
Takanobu Yanagihara, Hotaka Takizawa (Univ. of Tsukuba) MI2011-55
In this report, we propose a detection method of pulmonary nodules in X-ray CT images using discriminant filters and k-m... [more] MI2011-55
pp.41-46
MI 2011-07-12
10:30
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
pp.7-10
MI 2011-01-20
11:30
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
pp.101-104
MI 2010-01-29
11:40
Okinawa Naha-Bunka-Tenbusu Automatic classification method of Solid and Ground Glass Opacity in Chest X-ray CT Images
Takuya Tomida, Takeshi Hara, Xiangrong Zhou, Tatsuro Hayashi, Chisako Muramatsu, Hiroshi Fujita (Gifu Univ.) MI2009-150
(To be available after the conference date) [more] MI2009-150
pp.397-400
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