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Committee Date Time Place Paper Title / Authors Abstract Paper #
MI 2021-07-08
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.)
PRMU 2020-12-17
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
PRMU, IPSJ-CVIM 2020-03-17
(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
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
MI 2019-01-22
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
MICT, MI 2018-11-06
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
MICT, MI 2018-11-06
Hyogo University of Hyogo MICT2018-49 MI2018-49 (To be available after the conference date) [more] MICT2018-49 MI2018-49
MICT, MI 2018-11-06
Hyogo University of Hyogo MICT2018-50 MI2018-50 (To be available after the conference date) [more] MICT2018-50 MI2018-50
IBISML 2017-11-09
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
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2017-06-24
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
PRMU, IE, MI, SIP 2017-05-26
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
PRMU, CNR 2017-02-19
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
PRMU, CNR 2017-02-19
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
PRMU, CNR 2017-02-19
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
PRMU, CNR 2017-02-19
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
PRMU, CNR 2017-02-19
Hokkaido   [Poster Presentation] Online algorithm of swallowing detection using close-range depth sensor
Tsubasa Takai, Tomoya Sakai, Misako Higashijima (Nagasaki Univ) PRMU2016-183 CNR2016-50
We are developing an online algorithm of detecting and counting swallowing motions from a depth image sequence
for cont... [more]
PRMU2016-183 CNR2016-50
PRMU, CNR 2017-02-19
Hokkaido   [Poster Presentation] Identifying destinations of short messages via sparse representation of word vectors
Satoru Fukushima, Tomoya Sakai, Toru Kobayashi (Nagasaki Univ.) PRMU2016-184 CNR2016-51
 [more] PRMU2016-184 CNR2016-51
IT, SIP, RCS 2017-01-20
Osaka Osaka City Univ. [Invited Lecture] Online algorithms of low-rank and sparse structure pursuit in practice
Tomoya Sakai (Nagasaki Univ.) IT2016-95 SIP2016-133 RCS2016-285
 [more] IT2016-95 SIP2016-133 RCS2016-285
IBISML 2016-11-17
Kyoto Kyoto Univ. Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data
Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu (UTokyo), Masashi Sugiyama (RIKEN/UTokyo) IBISML2016-80
Most of the semi-supervised learning methods developed so far use unlabeled data for regularization purposes under parti... [more] IBISML2016-80
MI, MICT 2016-09-16
Tokyo Koganei Campus, Tokyo University of Agriculture and Technology Object Oriented Data Analysis for Volumetric Medical Data using Multiway Principal Component Analysis
Hayato Itoh, Atsushi Imiya (Chiba Univ.), Tomoya Sakai (Nagasaki Univ.) MICT2016-43 MI2016-57
For object oriented data analysis of volumetric medical data, we introduce principal component analysis for the third or... [more] MICT2016-43 MI2016-57
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