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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 113  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
MI 2024-03-03
10:52
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Evaluation of underspecification in a hot-spot detection support system of bone scintigrams -- Comparison of pre-market learning and post-market re-learning --
Keisuke Fujimoto (TUAT), Shigeaki Higashiyama, Joji Kawabe (OMU), Ryusuke Nakaoka (NIHS), Akinobu Shimizu (TUAT) MI2023-38
(To be available after the conference date) [more] MI2023-38
pp.28-29
MI 2024-03-03
11:04
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
MI2023-39 (To be available after the conference date) [more] MI2023-39
pp.30-31
MI 2024-03-03
11:16
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
MI2023-40 (To be available after the conference date) [more] MI2023-40
pp.32-33
MI 2024-03-03
13:15
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
[Special Talk] Deployment of a computer-aided diagnosis system for bone scintigrams and beyond
Akinobu Shimizu (TUAT) MI2023-44
 [more] MI2023-44
p.42
MI 2024-03-03
16:30
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Conditional statistical shape models for multiple brain regions of human embryos
Nao Chikaarashi (TUAT), Tetsuya Takakuwa, Shigehito Yamada (Kyoto Univ.), Akinobu Shimizu (TUAT) MI2023-56
(To be available after the conference date) [more] MI2023-56
pp.77-78
MI 2023-03-06
09:05
Okinawa OKINAWA SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Accuracy improvement of emsemble learning based dementia classification for brain SPECT volumes
Naoki Matsumoto (TUAT), Makoto Fukasawa, Tomomichi Iizuka (Fukujuji Hosp.), Akinobu Shimizu (TUAT) MI2022-72
(To be available after the conference date) [more] MI2022-72
pp.1-2
MI 2023-03-06
10:23
Okinawa OKINAWA SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Computer-aided diagnosis of chest CT images for COVID-19 with lesion enhancement
Yusuke Takateyama (TUAT), Masahiro Hashimoto (Keio Univ.), Yoshito Otake (NAIST), Toshiaki Akashi (Juntendo Univ.), Akinobu Shimizu (TUAT) MI2022-78
(To be available after the conference date) [more] MI2022-78
pp.24-25
MI 2023-03-06
16:51
Okinawa OKINAWA SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Synthesizing COVID-19 CT Images with Generative Adversarial Networks
Ryo Kawasaki (TUAT), Masahiro Hashimoto (Keio Univ.), Yoshito Otake (NAIST), Toshiaki Akashi (Juntendo Univ.), Akinobu Shimizu (TUAT) MI2022-95
(To be available after the conference date) [more] MI2022-95
pp.111-112
MI 2023-03-07
09:35
Okinawa OKINAWA SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Performance analysis of a hot-spot detection support system of bone scintigrams by post-market training
Kaho Shimada (TUAT), Hiromitsu Daisaki (GCHS), Shigeaki Higashiyama, Joji Kawabe (OMU), Ryusuke Nakaoka (NIHS), Akinobu Shimizu (TUAT) MI2022-106
(To be available after the conference date) [more] MI2022-106
pp.139-140
MI 2023-03-07
17:16
Okinawa OKINAWA SEINENKAIKAN
(Primary: On-site, Secondary: Online)
[Short Paper] Anomaly Region Detection for Chest CT Images based on Probability Density Estimation
Hiroki Tobise, Mauricio Kugler, Tatsuya Yokota (NIT), Masahiro Hashimoto (Keio Univ.), Yoshito Otake (NAIST), Toshiaki Akashi (Juntendo Univ.), Akinobu Shimizu (TUAT), Hidekata Hontani (NIT) MI2022-127
 [more] MI2022-127
pp.215-216
MI 2022-01-25
15:22
Online Online MI2021-46 (To be available after the conference date) [more] MI2021-46
pp.14-15
MI 2022-01-26
10:00
Online Online MI2021-52 In this report, we propose a method for constructing a statistical intensity model of blood vessels in thoracic CT image... [more] MI2021-52
pp.39-40
MI 2022-01-26
10:13
Online Online [Short Paper] Abnormality Detection for Covid-19 Chest CT Images by Dimensionality Reduction Based on Contrastive Learning
Hiroki Tobise, Kugler Mauricio, Tatsuya Yokota (NITech), Masahiro Hashimoto (Keio Univ.), Yoshito Otake (NAIST), Toshiaki Akashi (Juntendo Univ.), Akinobu Shimizu (TUAT), Hidekata Hontani (NITech) MI2021-53
In this article, we propose a method that detects anomaly regions in chest CT images for the aid of Covid-19 diagnosis. ... [more] MI2021-53
pp.41-42
MI 2022-01-26
10:26
Online Online MI2021-54  [more] MI2021-54
pp.43-44
MI 2022-01-27
16:00
Online Online MI2021-86 The appearance of skin diseases varies greatly depending on the type, location, and severity of the disease. There have ... [more] MI2021-86
pp.184-185
MI 2022-01-27
16:26
Online Online MI2021-88 (To be available after the conference date) [more] MI2021-88
pp.188-189
MI 2021-03-15
11:00
Online Online Improvement of a skeletal recognition algorithm of bone scintigrams
Yuri Hoshino, Atsushi Saito (TUAT), Atsushi Yoshida, Shigeaki Higashiyama, Joji Kwabe (Osaka City Univ), Hiromitsu Daisaki (GCHS), Kazuhiro Nishikawa (NMP), Akinobu Shimizu (TUAT) MI2020-47
In this paper, we report the results of our work on skeletal recognition processing on bone scintigram. Specifically, we... [more] MI2020-47
pp.1-2
MI 2021-03-16
09:00
Online Online Lung segmentation of thoracic CT volumes by combining slice-wise with pixel-wise classification
Takahito Haruishi, Atsushi Saito (TUAT), Masahiro Hashimoto (Keio University), Yoshito Otake (NAIST), Toshiaki Akashi (Juntendo University), Akinobu Shimizu (TUAT) MI2020-63
 [more] MI2020-63
pp.72-73
MI 2021-03-16
09:30
Online Online Statistical modeling of pulmonary vasculatures in CT volumes using a deep generative model
Yuki Saeki, Atshushi Saito (TUAT), Jean Cousty, Yukiko Kenmochi (LIGM/ UGE/ CNRS/ ESIEE Paris), Akinobu Shimizu (TUAT) MI2020-65
The purpose of this study is to build a statistical intensity model of pulmonary vasculatures in CT volumes. In this stu... [more] MI2020-65
pp.80-81
MI 2021-03-17
09:45
Online Online Improvement of deep learning based dementia classification using brain SPECT volumes
Mitsuru Yambe, Atsushi Saito (TUAT), Makoto Fukasawa, Tomomichi Iizuka (Fukujuji Hospital), Akinobu Shimizu (TUAT) MI2020-86
In this study, we propose a deep network to classify 3D cerebral blood flow SPECT volumes into four types: three types o... [more] MI2020-86
pp.164-165
 Results 1 - 20 of 113  /  [Next]  
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