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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI | 2021-03-17 11:00 |
Online | Online | Optimal Design and Quality Assessment of Color Laparoscopic Super-Resolution Image by Generative Adversarial Networks Norifumi Kawabata (Tokyo Univ. of Science), Toshiya Nakaguchi (Chiba Univ.)  MI2020-91 |
The Generative Adversarial Networks (GAN) is unsupervised learning enabled to transform according to data characteristic... [more] | MI2020-91 pp.186-190 |
| NC, MBE (Joint) |
2021-03-03 15:35 |
Online | Online | A Study on Feature Extraction of signal arrival order using unsupervised learning of the pulsed neuron model Kaya Teramoto, Susumu Kuroyanagi (NIT) NC2020-51 |
For time series information processing using pulsed neuron models, a supervised learning rule is proposed that enables c... [more] | NC2020-51 pp.47-52 |
| KBSE | 2021-01-23 15:00 |
Online | Online | Consideration of evaluation datasets for DNS tunnel detection research Tetsuya Asakura, Takeo Tatsumi (OUJ) KBSE2020-32 |
In this research, we considered of evaluation datasets for dns tunnel detection research. In this field, there are not ... [more] |
KBSE2020-32 pp.19-24 |
| PRMU | 2020-12-17 14:55 |
Online | Online | Improving the accuracy of unsupervised segmentation by introducing a Laplacian filter loss function
-- Application to automotive wire harness components -- Yuki Matsumoto (SEI) PRMU2020-45 |
Semantic segmentation, in which images are classified into pixel-by-pixel classes by deep learning, has been widely stud... [more] | PRMU2020-45 pp.42-46 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2020-11-17 14:25 |
Online | Online | Energy-Efficient ECG Signals Outlier Detection Hardware Using a Sparse Robust Deep Autoencoder Naoto Soga, Shimpei Sato, HIroki Nakahara (Tokyo Tech) VLD2020-17 ICD2020-37 DC2020-37 RECONF2020-36 |
Advancements in portable electrocardiographs have allowed electrocardiogram (ECG) signals to be recorded in everyday lif... [more] | VLD2020-17 ICD2020-37 DC2020-37 RECONF2020-36 pp.36-41 |
| MI | 2020-09-03 10:00 |
Online | Online | Lung region segmentation of thoracoscopic image with unsupervised image translation Jumpei Nitta, Megumi Nakao (Kyoto Univ.), Keiho Imanishi (e-Growth Co. Ltd.), Tetsuya Matsuda (Kyoto Univ.) MI2020-19 |
In endoscopic surgery, it is necessary to understand the three-dimensional structure of the target region to improve saf... [more] | MI2020-19 pp.13-18 |
| MI | 2020-09-03 14:25 |
Online | Online | Proposal of 3D Generative Adversarial Network for Improving Image Ouality of Cone-Beam CT Images Takumi Hase, Megumi Nakao (Kyoto Univ.), Keoho Imanishi (e-Growth Co., Ltd), Mitsuhiro Nakamura, Tetsuya Matsuda (Kyoto Univ.) MI2020-29 |
Artifacts and defects included in Cone-beam CT (CBCT) images have become an obstacle in radiation therapy and surgery su... [more] | MI2020-29 pp.51-56 |
| ISEC, IT, WBS | 2020-03-10 13:25 |
Hyogo | University of Hyogo (Cancelled but technical report was issued) |
Research on DNS tunnel detection by machine learning using appearance characters
-- Consideration of implementation of evaluation program -- Tetsuya Asakura, Takeo Tatsumi (OUJ) IT2019-103 ISEC2019-99 WBS2019-52 |
In this study, we considered an implementation a detection technique of DNS tunnel. This detection techniqe is likely t... [more] |
IT2019-103 ISEC2019-99 WBS2019-52 pp.87-94 |
| SP, EA, SIP | 2020-03-03 09:00 |
Okinawa | Okinawa Industry Support Center (Cancelled but technical report was issued) |
[Poster Presentation]
Comparison of Neural Network Models for Detection of Spatiotemporal Abnormal Intervals in Epileptic EEG Kosuke Fukumori (TUAT), Noboru Yoshida (Juntendo Univ.), Toshihisa Tanaka (TUAT) EA2019-156 SIP2019-158 SP2019-105 |
Epilepsy is a chronic brain disease, and the detection of abnormal waveforms by scalp electroencephalography (EEG) is an... [more] | EA2019-156 SIP2019-158 SP2019-105 pp.319-323 |
| MI | 2020-01-29 10:05 |
Okinawa | OKINAWAKEN SEINENKAIKAN | A study of generalized generation of image features for computer-aided detection systems based on unsupervised learning with normal datasets
-- Experimental evaluations of feature generation by small datasets -- Kazuyuki Ushifusa, Mitsutaka Nemoto(, Yuichi Kimura, Takashi Nagaoka, Takahiro Yamada, Atsuko Tanaka (Kindai Uni.), Naoto Hayashi (The Uni of Tokyo Hosp) MI2019-68 |
In a computer-aided detection system, image features are essential factors. In this study, we propose an image feature g... [more] | MI2019-68 pp.15-18 |
| MI | 2020-01-30 10:40 |
Okinawa | OKINAWAKEN SEINENKAIKAN | Evaluation of 3D adversarial networks for metallic dental artifact reduction Megumi Nakao (Kyoto Univ.), Keiho Imanishi (e-Growth), Nobuhiro Ueda (Nara Medical Univ.), Yuichiro Imai (Otowa Hosp.), Tadaaki Kirita (Nara Medical Univ.), Tetsuya Matsuda (Kyoto Univ.) MI2019-101 |
(To be available after the conference date) [more] | MI2019-101 pp.159-164 |
| ISEC, SITE, LOIS | 2019-11-02 15:25 |
Osaka | Osaka Univ. | Research on DNS tunnel detection by machine learning using appearance characters Tetsuya Asakura, Takeo Tatsumi (OUJ) ISEC2019-84 SITE2019-78 LOIS2019-43 |
In this study, as a detection technique of DNS tunnel, it was tried to detect abnormal DNS query string by machine learn... [more] | ISEC2019-84 SITE2019-78 LOIS2019-43 pp.141-148 |
| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-05 10:20 |
Okayama | Metal artifact reduction using CycleGAN for CT images Megumi Nakao (Kyoto Univ.), Kieho Imanishi (e-Grwoth), Nobuhiro Ueda (Nara Med.), Yuichiro Imai (Otowa Hosp.), Tadaaki Kirita (Nara Med.), Tetsuya Matsuda (Kyoto Univ.) PRMU2019-23 MI2019-42 |
(To be available after the conference date) [more] | PRMU2019-23 MI2019-42 pp.63-68 |
|
| AI, IPSJ-ICS, JSAI-KBS, JSAI-DOCMAS, JSAI-SAI | 2019-03-09 17:20 |
Hokkaido | Please fill in Fumiya Kudo (SyntheMec), Souichiro Yokoyama, Tomohisa Yamashita, Hidenori Kawamura (Hokudai) AI2018-58 |
Although inspection of defective products is generally conducted visually at the manufacturing site of industrial produc... [more] | AI2018-58 pp.31-36 |
|
| HWS, VLD | 2019-02-28 13:55 |
Okinawa | Okinawa Ken Seinen Kaikan | Model Compression for ECG Signals Outlier Detection Hardware trained by Sparse Robust Deep Autoencoder Naoto Soga, Shimpei Sato, Hiroki Nakahara (Titech) VLD2018-114 HWS2018-77 |
In recent years, portable electrocardiographs and wearable devices have begun to spread so that electrocar- diogram (ECG... [more] | VLD2018-114 HWS2018-77 pp.127-132 |
| MI | 2019-01-23 14:00 |
Okinawa | Unsupervised Shadow Detection for Ultrasound Images by Deep Learning Suguru Yasutomi (FLL), Akira Sakai (FATEC), Masaaki Komatsu (Riken), Ryu Matsuoka, Reina Komatsu, Tatsuya Arakaki, Mayumi Tokunaka (Showa-U), Hidenori Machino, Kazuma Kobayashi (NCC), Ken Asada (Riken), Syuzo Kaneko (NCC), Akihiko Sekizawa (Showa-U), Ryuji Hamamoto (Riken) MI2018-96 |
Medical ultrasound is widely used for diagnosing internal organs since it is non-invasive. Shadows are often appear in u... [more] | MI2018-96 pp.151-156 |
|
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Realizing Large Scale Model by Integration of Stochastic Models
-- Implementation and Evaluation of Integrated Model of VAE, GMM, HMM and MLDA -- Ryo Kuniyasu, Tomoaki Nakamura, Tatsuya Aoki (UEC), Akira Taniguchi, Ryo Ozaki, Tomoro Ishimine (Ritsumeikan Univ.), Hiroki Yokoyama (Tamagawa Univ.), Tadashi Ogura (SOKENDAI), Takayuki Nagai (UEC), Tadahiro Taniguchi (Ritsumeikan Univ.) IBISML2018-77 |
In order to realize human-like intelligence artificially, large-scale cognitive models are required for robots to unders... [more] | IBISML2018-77 pp.249-254 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Tensor decomposition based unsupervised feature extraction applied to bioinformatics Y-h. Taguchi (Chuo Univ.) IBISML2018-90 |
Although supervised and reinforcement learning including deap learning performs excellent achievements, it is not applic... [more] | IBISML2018-90 pp.345-352 |
| AI | 2018-08-27 15:50 |
Osaka | Bayesian Inference for Field of Physical Quantity from Data obtained at several Locations Masato Ota, Takeshi Okadome (KG Univ.) AI2018-23 |
This paper proposes a novel method for estimating the physical quantity at every location (physical quan- tity field) fr... [more] | AI2018-23 pp.55-60 |
|
| SP | 2018-08-27 14:20 |
Kyoto | Kyoto Univ. | [Invited Talk]
Product models and semi-supervised word segmentation Daichi Mochihashi (ISM) SP2018-28 |
While deep learning methods have achieved revolutionary success in speech and audio research, the impact is less signif... [more] |
SP2018-28 p.29 |
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