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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 33  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
SS 2024-03-09
13:55
Okinawa
(Primary: On-site, Secondary: Online)
A Study on Cloud-Based Image Generation Using Spot Instances
Kippei Kasai, Daisuke Katayama, Takahiro Koita (Doshisha Univ.) SS2023-83
Recently, cloud computing has become an important service in the IT industry. However, there is a challenge for organiza... [more] SS2023-83
pp.202-206
MI 2024-03-03
09:17
Okinawa OKINAWAKEN SEINENKAIKAN
(Primary: On-site, Secondary: Online)
[Short Paper] Valid p-value for critical instances in multiple instance learning
Noriaki Hashimoto (RIKEN), Daiki Miwa (Nitech), Kosei Sumida (Nagoya Univ.), Hiroyuki Hanada (RIKEN), Hiroaki Miyoshi (Kurume Univ.), Jun Sakuma (Tokyo Tech/RIKEN), Hidekata Hontani (Nitech), Koichi Ohshima (Kurume Univ.), Ichiro Takeuchi (Nagoya Univ./RIKEN) MI2023-31
(To be available after the conference date) [more] MI2023-31
pp.3-6
PRMU, IBISML, IPSJ-CVIM 2024-03-03
09:12
Hiroshima Hiroshima Univ. Higashi-Hiroshima campus
(Primary: On-site, Secondary: Online)
Divide-and-Conquer Strategy Based Petal Segmentation in CT Images
Yuki Naka, Yuzuko Utsumi, Masakazu Iwamura (Osaka Metropolitan Univ.), Hirokazu Tsukaya (Tokyo Univ.), Koichi Kise (Osaka Metropolitan Univ.) PRMU2023-58
(To be available after the conference date) [more] PRMU2023-58
pp.41-46
PRMU, IBISML, IPSJ-CVIM 2024-03-03
15:12
Hiroshima Hiroshima Univ. Higashi-Hiroshima campus
(Primary: On-site, Secondary: Online)
Multi-class Multi-instance Learning
Kaito Shiku, Kazuya Nishimura (Kyushu Univ.), Daiki Suehiro (YCU), Ryoma Bise (Kyushu Univ.) PRMU2023-61
(To be available after the conference date) [more] PRMU2023-61
pp.59-63
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] 2023-06-29
14:45
Okinawa OIST Conference Center
(Primary: On-site, Secondary: Online)
Multiple Instance Learning with major class
Kaito Shiku, Shinnosuke Matsuo (Kyushu Univ), Daiki Suehiro (YCU), Bise Ryoma (Kyushu Univ) NC2023-4 IBISML2023-4
(To be available after the conference date) [more] NC2023-4 IBISML2023-4
pp.25-29
SS 2023-03-15
16:45
Okinawa
(Primary: On-site, Secondary: Online)
Cost-performance evaluation of instances for execution of Scientific Workflows
Mitsuki Ogawa, Taichi Sugimura, Takahiro Koita (Doshisha Univ.) SS2022-73
The cloud environment is expected to be an ideal environment for scientific and technological computing workflows that a... [more] SS2022-73
pp.157-161
ITS, IEE-ITS 2023-03-13
16:20
Chiba Nihon Univ., Funabashi Campus
(Primary: On-site, Secondary: Online)
Prediction of the Future Location of a Vehicle captured from the Dashboard Camera using Instance Segmentation
Koki Ikeda, Takumi Uemura, Shuichi Ojima (Sojo Univ.) ITS2022-83
In recent years, the development of automated driving has been active in the automated driving industry, and Level 3 and... [more] ITS2022-83
pp.22-27
IN, NS
(Joint)
2023-03-02
12:50
Okinawa Okinawa Convention Centre + Online
(Primary: On-site, Secondary: Online)
Consideration of bidding method for spot instances on AWS regions
Kippei Kasai (Doshisha Univ.), Daisuke Katayama, Takahiro Koita (Grad.Sch.Engineering Doshisha Univ.) IN2022-74
One of the advantages of using the cloud is cost reduction, and AWS offers EC2, a virtual computing cloud, as spot insta... [more] IN2022-74
pp.51-56
PRMU 2022-12-15
15:30
Toyama Toyama International Conference Center
(Primary: On-site, Secondary: Online)
Training Method for Image-based Instance Segmentation by Video-based Object-Centric Representation Learning
Tomokazu Kaneko, Ryosuke Sakai, Soma Shiraishi (NEC) PRMU2022-40
Object-centric representation learning (OCRL) aims to separate and extract object-wise representations from an image.
... [more]
PRMU2022-40
pp.43-48
MI 2022-07-08
14:00
Hokkaido
(Primary: On-site, Secondary: Online)
Cell type-specific tumor degree estimation in malignant lymphoma pathology images
Hiroki Masuda (NITech), Noriaki Hashimoto (RIKEN), Yusuke Takagi (NITech), Hiroyuki Hanada (RIKEN), Hiroaki Miyoshi, Kensaku Sato, Koichi Oshima (Kurume Univ.), Hidekata Hontani (NITech), Ichiro Takeuchi (Nagoya Univ./RIKEN) MI2022-32
In the pathological diagnosis flow of malignant lymphoma, a type of blood cancer, it is important to identify the type o... [more] MI2022-32
pp.1-6
CQ, IMQ, MVE, IE
(Joint) [detail]
2022-03-09
09:45
Online Online (Zoom) Improving Weakly Supervised Instance Segmentation by Encoding Motion Information via Optical Flow
Jun Ikeda, Junichiro Mori (UT) IMQ2021-15 IE2021-77 MVE2021-44
Weakly supervised instance segmentation is an important task that can significantly reduce the annotation cost of model ... [more] IMQ2021-15 IE2021-77 MVE2021-44
pp.27-32
MVE 2021-09-17
14:30
Online Online Improving Mask Generation Accuracy Exploiting Optical Flow in Weakly Supervised Instance Segmentation
Jun Ikeda, Junichiro Mori (UTokyo) MVE2021-15
Weakly supervised instance segmentation is important because it reduces the huge pixel-level annotation cost required to... [more] MVE2021-15
pp.38-43
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
HCGSYMPO
(2nd)
2019-12-11
- 2019-12-13
Hiroshima Hiroshima-ken Joho Plaza (Hiroshima) Image Synthesis from Instance Map
Ryoka Oishi, Kyoko Sudo (Toho Univ.)
We propose a new network of image synthesis. The network generates multiple instances with variations from the semantic ... [more]
PRMU, MI, IPSJ-CVIM [detail] 2019-09-04
16:20
Okayama   Domain-adversarial multiple instance learning for subtype classification of malignant lymphoma
Daisuke Fukushima, Ryoichi Koga, Noriaki Hashimoto, Kaho Ko (Nagoya Inst. of Tech), Masato Nakaguro, Kei Kohno, Shigeo Nakamura (Nagoya Univ. Hospital), Hidekata Hontani (Nagoya Inst of Tech), Ichiro Takeuchi (Nagoya Inst. of Tech/RIKEN/NIMS) PRMU2019-15 MI2019-34
We classify subtypes of malignant lymphoma using convolutional neural network with digital pathological images as input ... [more] PRMU2019-15 MI2019-34
pp.19-24
LOIS 2019-03-08
14:20
Okinawa Miyakojima-shi Central Community Center Examination of eye-camera image analysis method using Mask R-CNN
Yuto Yoshikawa, Yukikazu Murakami (NIT, Kagawa), Kazuaki Shiraishi, Gai Shibahara (NIT,Toba) LOIS2018-76
In recent years, opportunities for new farmers to receive direct guidance from experienced farmers have been decreasing ... [more] LOIS2018-76
pp.121-125
MI 2018-07-24
15:25
Iwate aiina (Morioka, Iwate) MI2018-30 (To be available after the conference date) [more] MI2018-30
pp.45-49
SIP, EA, SP, MI
(Joint) [detail]
2018-03-19
13:40
Okinawa   MI2017-78 Diseases appearing in the spine include spondylolysis, spondylolisthesis, vertebral fracture, and the like. A preoperati... [more] MI2017-78
pp.43-44
PRMU, MVE, IPSJ-CVIM [detail] 2018-01-18
17:15
Osaka  
Teruaki Fujiyoshi (Kyushu Univ.), Rika Motodate, Toshiharu Suzuki (Hokkaido Univ.), Seiichi Uchida (Kyushu Univ.) PRMU2017-127 MVE2017-48
(To be available after the conference date) [more] PRMU2017-127 MVE2017-48
pp.129-134
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
 Results 1 - 20 of 33  /  [Next]  
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