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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI | 2024-03-04 10:46 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Automated musculoskeletal segmentation of torso CT images Sanaa Amina Gourine, Mazen Soufi, Yoshito Otake (NAIST), Yuto Masaki (NAIST-PSP Corporation), Yoko Murakami, Yukihiro Nagatani, Yoshiyuki Watanabe (Shiga Univ), Keisuke Uemura (Osaka Univ), Masaki Takao (Ehime Univ), Nobuhiko Sugano (Osaka Univ), Yoshinobu Sato (NAIST)  MI2023-70 |
Musculoskeletal segmentation (MSK) in CT is helpful for several applications, including body composition analysis, biome... [more] | MI2023-70 pp.122-126 |
| MI | 2023-03-06 13:15 |
Okinawa | OKINAWA SEINENKAIKAN (Primary: On-site, Secondary: Online) |
[Short Paper]
Improvement of Small Organ Accuracy in Multi-Organ Segmentation of Abdominal CT Images Using 2.5D Deformable Convolutional CNN Yuya Okumura, Hiroyuki Kudo, Hotaka Takizawa (Univ of Tsukuba) MI2022-80 |
In multi-organ segmentation of abdominal CT images using deep learning, small organs such as the pancreas are difficult ... [more] | MI2022-80 pp.38-39 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-20 16:40 |
Kumamoto | Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
3D Medical Image Segmentation Using 2.5D Deformable Convolutional CNN Yuya Okumura, Kudo Hiroyuki, Takizawa Hotaka (Tsukuba Univ.) SIP2022-29 BioX2022-29 IE2022-29 MI2022-29 |
An effective method to improve the accuracy of 3D medical image segmentation using deep learning is to use deformable co... [more] | SIP2022-29 BioX2022-29 IE2022-29 MI2022-29 pp.150-155 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-20 17:00 |
Kumamoto | Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
Deformable registration of 3D medical images with Deep Residual UNet Taiga Nakamura, Yuki Sato, Hiroyuki Kudo, Hotaka Takizawa (Univ. of Tsukuba) SIP2022-30 BioX2022-30 IE2022-30 MI2022-30 |
(To be available after the conference date) [more] | SIP2022-30 BioX2022-30 IE2022-30 MI2022-30 pp.156-160 |
| MI | 2022-01-27 16:39 |
Online | Online | [Short Paper]
Multiple Organ Detection from CT Images Based on Deep Learning
-- Fusion of 2D-CNN and Transformer -- Daiki Kanoh, Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita (Gifu Univ.) MI2021-89 |
The automatic recognition of multiple organs in 3D CT images and detecting organ positions are required for computer-aid... [more] | MI2021-89 pp.190-193 |
| MI | 2021-07-08 13:30 |
Online | Online | [Short Paper]
Performance comparison of multiple deep CNN methods for multiple organ detection in CT images Daiki Kanoh, Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita (Gifu Univ.) MI2021-10 |
The scheme of automatically recognizing multiple organs and detecting their localizations in 3D CT images is required fo... [more] | MI2021-10 pp.7-10 |
| MI | 2021-05-17 15:10 |
Online | Online | [Short Paper]
Fundamental study of automatic segmentation of skeletal muscle regions on whole body CT images based on a 3D DeepCNN Kota Nozaki, Xiangong Zhou (Gifu Univ.), Naoki Kamiya (Aichi Prefectual Univ.), Takeshi Hara, Hiroshi Fujita (Gifu Univ.) MI2021-7 |
Amyotrophic lateral sclerosis (ALS) is an intractable disease in which voluntary muscles atrophy gradully due to degener... [more] | MI2021-7 pp.20-22 |
| MI | 2021-03-16 09:45 |
Online | Online | MI2020-66 |
In this paper, we propose a method for automatically classifying COVID-19 cases from CT images of lung fields. Currently... [more] | MI2020-66 pp.82-86 |
| MI | 2021-03-17 10:45 |
Online | Online | [Short Paper]
Preliminary study for improving the performance of abdominal multi-phase CT image registration based on 3D deep CNN with a CycleGAN Ryotaro Fuwa, Xiangong Zhou, Takeshi Hara, Hiroshi Fujita (Gifu Univ.) MI2020-90 |
Deep learning is expected to be an approach to solve the problem of accurate medical image alignment. Recently, VoxelMor... [more] | MI2020-90 pp.182-185 |
| PRMU, IPSJ-CVIM | 2021-03-04 14:25 |
Online | Online | Leveraging Human Pose Estimation Model for Sports Video Classification Soichiro Sato, Masaki Aono (TUT) PRMU2020-76 |
In this paper, we propose a motion classification method of sports videos based on a posture estimation model.Specifical... [more] | PRMU2020-76 pp.41-46 |
| MVE, IMQ, IE, CQ (Joint) [detail] |
2021-03-03 09:20 |
Online | Online | Study of Environment Recognition and 3D Map Generation Using SegNet for Night Forest Monitoring Using an Environmental Monitoring Robot Takeo Kaneko (WASEDA Univ.), Junji Yamato (Kogakuin Univ.), Hiroyuki Ishii, Jun Ohya, Atuo Takanishi (WASEDA Univ.) IMQ2020-29 IE2020-69 MVE2020-61 |
Towards the actualization of autonomous robots that monitor forests, around which various kinds of damages caused by wil... [more] | IMQ2020-29 IE2020-69 MVE2020-61 pp.91-96 |
| RCS, SR, SRW (Joint) |
2020-03-04 09:20 |
Tokyo | Tokyo Institute of Technology (Cancelled but technical report was issued) |
CSI Feedback Overhead Reduction by 3D CNN for Time-varying FDD Massive MIMO Masumi Kuriyama, Tomoaki Ohtsuki (Keio Univ.) RCS2019-332 |
Massive MIMO (Multiple-Input Multiple-Output) is a technology that uses a large number of antennas at a base station (BS... [more] | RCS2019-332 pp.63-68 |
| MI | 2020-01-30 13:25 |
Okinawa | OKINAWAKEN SEINENKAIKAN | 2D Deep CNN for automated multi organ segmentation from CT images by using consecutive slices feature maps Hiroki Isakari, Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita (Gifu Univ.) MI2019-113 |
The development of a computer-aided diagnosis system is expected to reduce the burden on the radiologist in clinical pra... [more] | MI2019-113 pp.203-205 |
| IMQ | 2018-05-25 14:15 |
Chiba | Chibe Institute of Technology, Tsudanuma Campus | Optimal Design and Coded Image Quality Assessment of the Multi-view and Super-resolution Images Based on Structure of Convolutional Neural Network Norifumi Kawabata (Nagoya Univ.) IMQ2018-3 |
The image screen resolution by viewpoints is low, comparing to single-view images since there are many viewpoints for mu... [more] | IMQ2018-3 pp.15-20 |
| PRMU, BioX | 2017-03-21 14:10 |
Aichi | Three-dimensional shape retrieval based in voxel representation using LSTM and CNN Ryo Miyagi, Masaki Aono (TUT) BioX2016-67 PRMU2016-230 |
In recent years, with the spread of 3D scanners, VR headsets, etc., there is an increasing demand for recognition and re... [more] | BioX2016-67 PRMU2016-230 pp.203-208 |
|
| PRMU, CNR | 2017-02-19 10:20 |
Hokkaido | Research of Action Recognition for Automatic Internal State Estimation based on Deep Learning Kanji Yamaguchi, Masayuki Kashima, Shinya Fukumoto, Kiminori Satou, Mutsumi Watanabe (kagoshima Univ.) PRMU2016-177 CNR2016-44 |
Japan is turning into a high-stress society.Cause of stress are those that are in a state of mind taut by "anxiety-tensi... [more] | PRMU2016-177 CNR2016-44 pp.143-148 |
|
| PRMU, SP, WIT, ASJ-H | 2016-06-13 11:15 |
Tokyo | Preliminary study on deep manifold embedding for 3D object pose estimation Hiroshi Ninomiya, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase (Nagoya Univ.), Norimasa Kobori, Kunimatsu Hashimoto (Toyota) PRMU2016-39 SP2016-5 WIT2016-5 |
Recently, 3D object pose estimation is being focused. The parametric eigenspace method is known as one of the fundamenta... [more] | PRMU2016-39 SP2016-5 WIT2016-5 pp.25-30 |
|
| PRMU, IE, MI, SIP | 2016-05-19 13:30 |
Aichi | Automatic classification of breast density on CT images by using deep CNN Takuya Kano, Xiangrong Zhou (Gifu Univ.), Hiromi Koyasu (Gifu Univ. Hosp.), Ryuziro Yokoyama, Takeshi Hara (Gifu Univ.), Masayuki Matsuo (Gifu Univ. Hosp.), Hiroshi Fujita (Gifu Univ.) SIP2016-5 IE2016-5 PRMU2016-5 MI2016-5 |
Breast density has been used as an important risk factor of breast cancer and routinely measured on 2D mammography. 3D C... [more] | SIP2016-5 IE2016-5 PRMU2016-5 MI2016-5 pp.21-25 |
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