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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP | 2024-05-10 10:55 |
Kagawa | Kagawa Prefecture Social Welfare Center | Analysis of the role of latent variables in image classification in deep learning models Kenya Jin'no, Mizuki Dai, Haruki Wakasa, Hiroki Tamegai (Tokyo City Univ.) |
(To be available after the conference date) [more] | |
| RCC, ISEC, IT, WBS | 2024-03-13 09:15 |
Osaka | Osaka Univ. (Suita Campus) | Performance Evaluation of Visible Light Communication System Using Imaginary Images based Image Classifier Masataka Naito, Tadahiro Wada, Kaiji Mukumoto (Shizuoka Univ.), Hiraku Okada (Nagoya Univ.)  IT2023-78 ISEC2023-77 WBS2023-66 RCC2023-60 |
For visible light communication systems that utilizes machine learning-based image classifiers for information embedding... [more] | IT2023-78 ISEC2023-77 WBS2023-66 RCC2023-60 pp.20-25 |
| MI | 2024-03-03 16:42 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
[Short Paper]
Using Label Uncertainty for Learning Cell Nuclei Type Classifier with Strongly Noisy Supervised Signals Shingo Koide, Mauricio Kugler, Tatsuya Yokota (NIT), Koichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi (Kurume Univ.), Noriaki Hashimoto (RIKEN), Ichiro Takeuchi (Nagoya Univ.), Hidekata Hontani (NIT) MI2023-57 |
In this study, we construct a type classifier for cell nuclei of malignant lymphomas. Labelling by type is not easy, eve... [more] | MI2023-57 pp.79-80 |
| EMM | 2024-03-02 14:00 |
Overseas | Day1:JEJU TECHNOPARK, Day2:JEJU Business Agency | [Poster Presentation]
Classification of AI generated images by sparse coding Daishi Tanaka, Michiharu Niimi (KIT) EMM2023-89 |
In recent years, advancements in generative AI technologies have made it increasingly challenging for human vision to di... [more] | EMM2023-89 pp.1-6 |
| SIP, IT, RCS | 2024-01-19 13:30 |
Miyagi | (Primary: On-site, Secondary: Online) |
[Invited Talk]
Problem of Adversarial Attacks on CNN-based Image Classifiers and Countermeasures Minoru Kuribayashi (Tohoku Univ.) IT2023-67 SIP2023-100 RCS2023-242 |
It is well-known that discriminative models based on deep learning techniques may cause misclassification if adversarial... [more] | IT2023-67 SIP2023-100 RCS2023-242 p.204 |
| MI | 2023-09-08 10:05 |
Osaka | (Primary: On-site, Secondary: Online) |
[Short Paper]
Construction of Cell Nucleus Classifier using Complementary-Label Learning towards the Quantification of Grading for Follicular Lymphoma Ryoichi Koga, Mauricio Kugler, Tatsuya Yokota (NIT), Kouichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi (Kurume Univ.), Noriaki Hashimoto (RIKEN), Ichiro Takeuchi (Nagoya Univ.), Hidekata Hontani (NIT) MI2023-14 |
In this paper, we report the cell type classification from a pathological image toward the subtype classification of mal... [more] | MI2023-14 pp.1-2 |
| RCC, ISEC, IT, WBS | 2023-03-14 09:50 |
Yamaguchi | (Primary: On-site, Secondary: Online) |
A Proposal of Visible Light Communication System using Image Classifier based on Imaginary Images Masataka Naito, Tadahiro Wada, Kaiji Mukumoto (Shizuoka Univ.), Hiraku Okada (Nagoya Univ.) IT2022-70 ISEC2022-49 WBS2022-67 RCC2022-67 |
We have proposed a new method of information embedding in visible light communication by using an image classifier based... [more] | IT2022-70 ISEC2022-49 WBS2022-67 RCC2022-67 pp.13-18 |
| MI | 2023-03-06 17:56 |
Okinawa | OKINAWA SEINENKAIKAN (Primary: On-site, Secondary: Online) |
[Short Paper]
Generation of Counterfactual Images towards the Construction of Quantitatively Criteria in Malignant Lymphoma Ryoichi Koga, Mauricio Kugler, Tatsuya Yokota (NIT), Kouichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi (KU), Noriaki Hashimoto, Ichiro Takeuchi (NU), Hidekata Hontani (NIT) MI2022-100 |
In pathological diagnosis of malignant lymphoma, a H&E-staind pathological image is observed to identify the subtype. Ho... [more] | MI2022-100 pp.123-124 |
| PRMU | 2022-12-15 10:45 |
Toyama | Toyama International Conference Center (Primary: On-site, Secondary: Online) |
DN4C
-- An Interactive Image Segmentation System Combining Deep Neural Network and Nearest Neighbor Classifier -- Toshikazu Wada, Koji Kamma (Wakayama University) PRMU2022-35 |
Color/texture based image segmentation can be widely applied to the images for product and/or medical inspection, remote... [more] | PRMU2022-35 pp.19-24 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-20 16:20 |
Kumamoto | Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
Visualization of Important Features for Classifier Decisions using Deep Image Synthesis Yushi Haku, Megumi Nakao, Tetsuya Matsuda (Kyoto Univ.) SIP2022-28 BioX2022-28 IE2022-28 MI2022-28 |
It is difficult to know the basis for the decisions of machine learning models, and it is necessary to provide a highly ... [more] | SIP2022-28 BioX2022-28 IE2022-28 MI2022-28 pp.144-149 |
| MBE, NC (Joint) |
2022-03-02 09:30 |
Online | Online | A Study on Improvement of Recognition Accuracy and Speed-up of SOM-based Classification System Shun Tasaka, Hiroomi Hikawa (Kansai Univ.) NC2021-46 |
This paper discusses a new type of image classifier called class-SOM, which is based on self-organizing map (SOM). The... [more] |
NC2021-46 pp.1-4 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-22 11:10 |
Online | Online | Enhancing Personalized Food Image Classifier by Visual Attention and Class-Dependent Weighting Seum Kim, Yoko Yamakata, Kiyoharu Aizawa (UTokyo) ITS2021-47 IE2021-56 |
In a real-world setting, food records are very noisy and strongly imbalanced. Besides, inter-class similarity and intra-... [more] | ITS2021-47 IE2021-56 pp.133-138 |
| 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 |
| PRMU | 2021-12-17 14:45 |
Online | Online | Data Selection for Efficient Deep Learning Ryota Higashi, Toshikazu Wada (Wakayama Univ.) PRMU2021-51 |
We are investigating the method to sample the important data from the whole dataset for efficient training of Deep Neura... [more] | PRMU2021-51 pp.148-153 |
| MI | 2021-07-09 11:00 |
Online | Online | [Short Paper]
Construction of Subtype Classifier for Malignant Lymphoma based on H&E-stained Images using Immuno-stainning Data Yuki Hirono (NIT), Noriaki Hashimoto (RIKEN), Kugler Mauricio, Tatsuya Yokota (NIT), Miharu Nagaishi (Kurume Univ.), Hiroaki Miyoshi, Koichi Oshima (Kurume Univ./JSP), Ichiro Takeuchi (NIT/RIKEN), Hidekata Hontani (NIT) MI2021-16 |
In pathological diagnosis of malignant lymphoma, a HE image is observed at first and then a set of immunostained images ... [more] | MI2021-16 pp.31-32 |
| SP, IPSJ-SLP, IPSJ-MUS | 2021-06-19 15:00 |
Online | Online | Development of ultrasonic signal classification system using deep learning Kosei Ozeki, Naofumi Aoki, Yoshinori Dobashi (Hokkaido Univ.), Kenichi Ikeda, Hiroshi Yasuda (SST) SP2021-21 |
The problem with sound wave communication is that it causes more incorrect identification than radio wave communication.... [more] | SP2021-21 pp.97-100 |
| EMM, IT | 2021-05-21 13:10 |
Online | Online | A Study of Detecting Adversarial Examples Using Sensitivities to Multiple Auto Encoders Yuma Yamasaki, Minoru Kuribayashi, Nobuo Funabiki (Okayama Univ.), Huy Hong Nguyen, Isao Echizen (NII) IT2021-11 EMM2021-11 |
By removing the small perturbations involved in adversarial examples, the image classification result returns to the cor... [more] | IT2021-11 EMM2021-11 pp.60-65 |
| MI | 2021-03-15 15:15 |
Online | Online | Deep State-Space Modeling of FMRI Images with Disentangle Attributes Koki Kusano (Kobe Univ.), Takashi Matsubara (Osaka Univ.), Kuniaki Uehara (Osaka Gakuin Univ.) MI2020-59 |
As well as the disorder and other targets, nuisance attributes such as age, gender, and scanner specifications underlie ... [more] | MI2020-59 pp.56-61 |
| EMM | 2021-03-04 14:15 |
Online | Online | [Poster Presentation]
Detection of Adversarial Examples in CNN Image Classifiers Using Features Extracted with Multiple Strengths of Filter Akinori Higashi, Minoru Kuribayashi, Nobuo Funabiki (Okayama Univ.), Huy Hong Nguyen, Isao Echizen (NII) EMM2020-70 |
Deep learning has been used as a new method for machine learning, and its performance has been significantly improved. A... [more] | EMM2020-70 pp.19-24 |
| MVE, IMQ, IE, CQ (Joint) [detail] |
2021-03-01 16:05 |
Online | Online | Taste Prediction System from Cooking Images Using Deep Learning Akinobu Yoshioka, Qiu Chen (kogakuin Univ.) IMQ2020-17 IE2020-57 MVE2020-49 |
In recent years, a large amount of food images have been uploaded on social media, etc., which have been closed related ... [more] | IMQ2020-17 IE2020-57 MVE2020-49 pp.34-39 |
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