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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MBE, MICT, IEE-MBE [detail] | 2023-01-17 09:50 |
Saga | Oral Cytology Based on Representation Learning of Visually Salient Cells Kazuki Matsuo, Eiji Mitate, Tomoya Sakai (Nagasaki Univ.)  MICT2022-44 MBE2022-44 |
We classify microscopically photographed cells for screening tests to find oral cancer in its early stages. Oral cancer ... [more] | MICT2022-44 MBE2022-44 pp.7-12 |
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| MI | 2022-09-15 14:00 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Unsupervised Cell Detection for Suppression of Background Information in Cytology Keita Takeda, Kazuki Matsuo, Kohei Fujiwara, Eiji Mitate, Tomoya Sakai (Nagasaki Univ.) MI2022-57 |
(To be available after the conference date) [more] | MI2022-57 pp.35-38 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-19 13:00 |
Kumamoto | Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
[Special Talk]
Integrative technologies of mathematical modeling and deep Learning
-- Three strategies to distill inductive biases -- Tomoya Sakai (Nagasaki Univ.) SIP2022-9 BioX2022-9 IE2022-9 MI2022-9 |
(To be available after the conference date) [more] | SIP2022-9 BioX2022-9 IE2022-9 MI2022-9 pp.49-54 |
| MI | 2022-01-25 15:45 |
Online | Online | Exploiting Perfusion MR Features for Salivary Gland Tumor Classification Keita Takeda, Misa Sumi, Tomoya Sakai (Nagasaki Univ.) MI2021-47 |
Learning perfusion features from dynamic contrast-enhanced MRI is effective for classification of salivary gland tumors.... [more] | MI2021-47 pp.16-21 |
| MI, MICT [detail] | 2021-11-05 10:00 |
Online | Online | [Short Paper]
Prediction of therapeutic response in Sjogren's syndrome using ultrasound images of parotid glands Kohei Fujiwara, Takeda Keita, Yukinori Takagi, Miho Sasaki, Sato Eida, Ikuo Katayama, Misa Sumi, Tomoya Sakai (Nagasaki Univ.) MICT2021-30 MI2021-28 |
The purpose of this study was to predict the response to treatment of SS from ultrasound (US) images of salivary glands ... [more] | MICT2021-30 MI2021-28 pp.15-16 |
| MI | 2021-07-08 14:00 |
Online | Online | Unsupervised deep learning with low-rank and sparse priors for blood vessel enhancement from free-breathing angiography Ryoji Ishibashi, Tomoya Sakai (Nagasaki Univ.), Hideaki Haneishi (Chiba Univ.) MI2021-11 |
(To be available after the conference date) [more] | MI2021-11 pp.11-14 |
| PRMU | 2020-12-17 17:15 |
Online | Online | Transfer learning from sparse models
-- Two approaches and optimization issues -- Tomoya Sakai, Rabi Yamada, Ryoji Ishibashi, Hiroyuki Takada (Nagasaki Univ.) PRMU2020-52 |
[more] | PRMU2020-52 pp.80-85 |
| PRMU, IPSJ-CVIM | 2020-03-17 09:45 |
Kyoto | (Cancelled but technical report was issued) |
Deep neural network representation and learning of low-rank and sparse approximation
-- With application to celiac angiography under free breathing -- Ryohei Miyoshi, Tomoya Sakai (Nagasaki Univ.), Takashi Ohnishi, Hideaki Haneishi (Chiba Univ.) PRMU2019-91 |
Low-rank and sparse (L+S) approximation, a.k.a. stable and robust principal component analysis, is known to be suitable ... [more] | PRMU2019-91 pp.133-138 |
| EMCJ, MICT (Joint) |
2020-03-13 13:40 |
Tokyo | Kikai-Shinko-Kaikan Bldg. (Cancelled but technical report was issued) |
Comparison of non-invasive measurement signals of neck surface deformation during repetitive saliva swallowing Yukari Miyata, Tomoya Sakai, Amane Yoshiki, Misako Higashijima (Nagasaki Univ) MICT2019-56 |
Pneumonia risk due to silent aspiration increases with age as the swallowing ability declines. A new daily-usable device... [more] | MICT2019-56 pp.23-27 |
| MI | 2019-01-22 09:35 |
Okinawa | Acceleration of angiographic region enhancement based on robust principal component analysis using parallel processing Morio Kawabe, Yuri Kokura, Takashi Ohnishi, Hideyuki Kato, Yoshihiko Ooka (Chiba Univ.), Tomoya Sakai (Nagasaki Univ.), Hideaki Haneishi (Chiba Univ.) MI2018-59 |
Robust principal component analysis (RPCA) can extract vessel information from consecutive digital angiographic images. ... [more] | MI2018-59 pp.1-4 |
|
| MICT, MI | 2018-11-06 14:50 |
Hyogo | University of Hyogo | Feature extraction of coarse/fine crackles and its improvement via sparse modeling techniques Kosei Nishitsuji, Tomoya Sakai, Toshikazu Fukumitsu, Yasushi Obase (Nagasaki Univ.), Sueharu Miyahara (BIPS) MICT2018-48 MI2018-48 |
Medical experts have heuristically defined lung sound features and validated their relations with patients’ conditions i... [more] | MICT2018-48 MI2018-48 pp.45-48 |
| MICT, MI | 2018-11-06 15:10 |
Hyogo | University of Hyogo | MICT2018-49 MI2018-49 |
(To be available after the conference date) [more] | MICT2018-49 MI2018-49 pp.49-53 |
| MICT, MI | 2018-11-06 15:30 |
Hyogo | University of Hyogo | MICT2018-50 MI2018-50 |
(To be available after the conference date) [more] | MICT2018-50 MI2018-50 pp.55-58 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning Tomoya Sakai, Gang Niu (UTokyo/RIKEN), Masashi Sugiyama (RIKEN/UTokyo) IBISML2017-40 |
Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classif... [more] | IBISML2017-40 pp.39-46 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] | 2017-06-24 10:20 |
Okinawa | Okinawa Institute of Science and Technology | Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags Han Bao (Univ. of Tokyo), Tomoya Sakai, Issei Sato (Univ. of Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2017-3 |
Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as b... [more] | IBISML2017-3 pp.55-62 |
| PRMU, IE, MI, SIP | 2017-05-26 10:20 |
Aichi | Deep Subspace Methods
-- Pattern Recognition using Hierarchical Structure of Linear Subspaces -- Hayato Itoh, Atsushi Imiya (Chiba Univ.), Tomoya Sakai (Ngasaki Univ.) SIP2017-18 IE2017-18 PRMU2017-18 MI2017-18 |
We introduce an new geodesic distance between images. This distance is defined as the Wasserstein distance between contr... [more] | SIP2017-18 IE2017-18 PRMU2017-18 MI2017-18 pp.93-98 |
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| PRMU, CNR | 2017-02-19 11:20 |
Hokkaido | [Poster Presentation]
Online Algorithm for Separating a Sequence of Optical Flow Fields with Low-rank and TV Regularization Shun Ogawa, Tomoya Sakai (Nagasaki Univ.) PRMU2016-179 CNR2016-46 |
Estimation of camera egomotion and detection of moving object both require separation of the apparent motions, a.k.a. t... [more] |
PRMU2016-179 CNR2016-46 pp.155-156 |
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| PRMU, CNR | 2017-02-19 11:20 |
Hokkaido | [Poster Presentation]
Representation and Separation of Respiratory Sounds by Online Robust Principal Component Analysis Kosei Nishitsuji, Shunpei Shiwa, Tomoya Sakai (Nagasaki Univ.) PRMU2016-180 CNR2016-47 |
This paper presents an online algorithm of separating lung sounds for computer-aided diagnosis. A lung sound consists of... [more] | PRMU2016-180 CNR2016-47 pp.157-158 |
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| PRMU, CNR | 2017-02-19 11:20 |
Hokkaido | [Poster Presentation]
Compressed Sensing for 4D-MRI
-- Fast Algorithm of Image Reconstruction -- Kohei Mochizuki, Tomoya Sakai (Nagasaki Univ.), Yukinojo Kitakami, Hideaki Haneishi (Chiba Univ.) PRMU2016-181 CNR2016-48 |
This work aims to reduce measurement time and improve the computational efficiency of four-dimensional magnetic resonanc... [more] | PRMU2016-181 CNR2016-48 pp.159-160 |
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| PRMU, CNR | 2017-02-19 11:20 |
Hokkaido | [Poster Presentation]
Computation of optical flow sequences with linear dependence and locality Hiroki Kuhara, Tomoya Sakai (Nagasaki Univ.) PRMU2016-182 CNR2016-49 |
[more] | PRMU2016-182 CNR2016-49 pp.161-162 |
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