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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 34  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU, IPSJ-CVIM, VRSJ-SIG-MR, MVE 2025-01-21
13:35
Fukuoka (Fukuoka, Online)
(Primary: On-site, Secondary: Online)
Anomaly Region Detection in Medical Images using Diffusion Models with Simplex Noise and Progressive Mask Refinement
Hiroki Tobise (NIT), Masahiro Hashimoto (Keio Univ.), Toshiaki Akashi (Juntendo Univ.), Hidekata Hontani (NIT) PRMU2024-36
In this paper, we propose an anomaly detection method that uses a diffusion model as an autoencoder. The diffusion model... [more] PRMU2024-36
pp.18-23
MICT, MI 2024-12-05
10:20
Osaka Jikei University of Health Care Sciences (Osaka) Fine-Tuning LLaVA-Med 1.5 for Automated Biomedical Report Generation on MRI Scans
Ahmed Tamer ELBoardy, Nouman Muhammad, Essam A. Rashed (UH) MICT2024-32 MI2024-30
This study explores the use ofLLaVA-Med1.5,a specialized large language
and vision model tailored for biomedicine,
... [more]
MICT2024-32 MI2024-30
pp.17-19
MI, MICT 2023-11-14
13:20
Fukuoka   (Fukuoka) Medical image diagnosis support system with image anonymization based on deep learning techniques
Katsuto Iwai, Ryuunosuke Kounosu (Toho Univ./AIST), Hirokazu Nosato (AIST), Yuu Nakajima (Toho Univ.) MICT2023-30 MI2023-23
When medical imaging AI models are hosted on cloud service there is a risk of sensitive medical images being leaked when... [more] MICT2023-30 MI2023-23
pp.21-24
MI, MICT 2023-11-14
14:00
Fukuoka   (Fukuoka) Estimating the degree of coronary artery stenosis from non-contrast CT images using a 3D convolution model -- Categorical approach --
Hiroki Shinoda, Tetsuya Asakawa (TUT), Kazuki Shimizu, Takuya Togawa, Kei Nomura (Toyohashi Heart Center), Masaki Aono (TUT) MICT2023-32 MI2023-25
In current medical images diagnosis, specialists take pictures of patients and search for the disease from the images. I... [more] MICT2023-32 MI2023-25
pp.29-32
CCS 2023-03-27
09:00
Hokkaido RUSUTSU RESORT (Hokkaido) Medical Image Segmentation with Inverse Heat Dissipation Model
Yu Kashihara, Takashi Matsubara (Osaka Univ.) CCS2022-82
The diffusion model is a generative model based on stochastic transitions and has been successfully used to generate
an... [more]
CCS2022-82
pp.107-112
MI 2023-03-06
17:04
Okinawa OKINAWA SEINENKAIKAN (Okinawa, Online)
(Primary: On-site, Secondary: Online)
[Short Paper] Rotation-Equivariant CNN for Medical Image Processing Applications
Ryota Ogino, Kugler Mauricio, Tatsuya Yokota, Hidekata Hontani (NITech) MI2022-96
In this study, we report an attempt to use a Rotation-Equivariant CNN to organize image data whose rotation direction an... [more] MI2022-96
pp.113-114
MI 2023-03-07
16:13
Okinawa OKINAWA SEINENKAIKAN (Okinawa, Online)
(Primary: On-site, Secondary: Online)
Classification of endoscope images with specular reflection using CNN
Shun Katsuyama, Masashi Fujii (Tottori Univ.), Kazutake Uehara (Yonago Coll.), Masaru Ueki, Hajime Isomoto, Katsuya Kondo (Tottori Univ.) MI2022-123
The endoscopic training system is required that checks whether the inspection points have been taken. In this report, we... [more] MI2022-123
pp.199-204
MBE, MICT, IEE-MBE [detail] 2023-01-17
10:40
Saga   (Saga) Potential problems that will arise for hospital LANs
Eisuke Hanada (Saga Univ.), Takato Kudou (Oita Univ.) MICT2022-46 MBE2022-46
Hospital Information Systems (HIS) have been introduced in almost all large hospitals. In addition to this, IP networks ... [more] MICT2022-46 MBE2022-46
pp.17-21
MI 2022-07-08
17:00
Hokkaido (Hokkaido, Online)
(Primary: On-site, Secondary: Online)
[Short Paper] Weakly-Supervised Focal Liver Lesion Detection in CT Images
He Li, Yutaro Iwamoto (Ritsumeikan Univ.), Xianhua Han (Yamaguchi Univ.), Lanfen Lin, Ruofeng Tong, Hongjie Hu (Zhejiang Univ.), Akira Furukawa (Tokyo Metropolitan Univ.), Shuzo Kanasaki (Koseikai Takeda Hospital), Yen-Wei Chen (Ritsumeikan Univ.) MI2022-40
Convolutional neural networks have been widely used for anomaly detection and one of their most common methods is autoen... [more] MI2022-40
pp.30-33
SC 2022-05-27
11:20
Online Online (Online) Developing a Secure Streaming System of Clinic Site for Medical Education
Sinan Chen, Masahide Nakamura, Kenji Sekiguchi (Kobe Univ.) SC2022-5
Clinical practice in the outpatient consultation room is restricted due to the COVID-19 control measures, resulting in t... [more] SC2022-5
pp.25-30
PRMU, IPSJ-CVIM 2022-03-10
10:40
Online Online (Online) Medical Image Captioning with Information based on Medical Concepts
Riku Tsuneda, Tetsuya Asakawa (TUT), Kazuki Shimizu, Takuyuki Komoda (THC), Masaki Aono (TUT) PRMU2021-64
Image Captioning for medical images is expected to augment the judgment of doctors and serve as a second opinion. Medica... [more] PRMU2021-64
pp.25-30
EST 2022-01-28
11:00
Online Online (Online) Blood Vessel Structure Analysis using a Simulation Model for the Purpose of Polyp Shape Recovery from Endoscopic Images
Shusuke Kato, Hiroyasu Usami, Akihiko Okazaki, Yuji Iwahori (Chubu Univ.), Ogasawara Naotaka, Kunio Kasugai (Aichi Medical Univ.) EST2021-83
In recent years, the incidence of colorectal cancer in Japan has been on the rise. It is essential to realize a medical ... [more] EST2021-83
pp.130-135
MI 2022-01-26
13:00
Online Online (Online) Relationship between Image Quality and Learning Effect in Color Laparoscopic Images Generation by Generative Adversarial Networks
Norifumi Kawabata (Hokkaido Univ.), Toshiya Nakaguchi (Chiba Univ.) MI2021-59
Improving of personal computer performance, it is possible for healthcare workers and related researchers to support for... [more] MI2021-59
pp.59-64
PRMU, IPSJ-CVIM 2021-03-04
16:20
Online Online (Online) VQA for Medical Image Data based on Image Feature Extraction and Fusion
Hideo Umada, Masaki Aono (TUT) PRMU2020-81
In recent years, there has been a remarkable growth in research on deep learning in the fields of computer vision and na... [more] PRMU2020-81
pp.71-76
PRMU 2020-12-18
16:20
Online Online (Online) [Short Paper] Case Discrimination: Self-supervised Learning for classification of Medical Image
Haohua Dong, Yutaro Iwamoto (Ritsumeikan Univ.), Xianhua Han (Yamaguchi Univ.), Lanfen Lin (Zhejiang Univ.), Hongjie Hu, Xiujun Cai (Sir Run Run Shaw Hospital), Yen-Wei Chen (Ritsumeikan Univ.) PRMU2020-64
Deep Learning provides exciting solutions to problems in medical image analysis and is regarded as a key method for futu... [more] PRMU2020-64
pp.151-155
PRMU 2020-10-09
10:45
Online Online (Online) Trial of three-dimensional extraction and classification of cell regions in the heart
Asuma Takematsu, Masahiro Migita, Masashi Toda, Yuichiro Arima (Kumamoto Univ.) PRMU2020-21
Analysis of cardiomyocytes is urgently needed to elucidate the pathophysiology of heart disease. Cardiomyocytes are char... [more] PRMU2020-21
pp.15-19
IMQ 2020-10-02
15:20
Online Online (Online) Development of software simulator for display design of 3D volumetric display
Du Leran (Chiba Univ.), Ryutaro Okamoto (Teidec), Shinsuke Akita, Yuichiro Yoshimura, Toshiya Nakaguchi (Chiba Univ.) IMQ2020-6
Intuitive understanding of the human body structure in three dimensions is important for diagnosis, treatment, informed ... [more] IMQ2020-6
pp.9-12
SC 2020-05-29
15:30
Online Online (Online) [Poster Presentation] Tumor detection from colonoscopy Whole Slice Images By Deep Learning
Cherubin Mugisha, Incheon Paik (School of Computer Science and Engineering)
Image semantic segmentation is a technique of segregating an image into many parts. The goal of this research was to use... [more]
MI 2020-01-29
10:15
Okinawa OKINAWAKEN SEINENKAIKAN (Okinawa) Analysis of disease classification and musculoskeletal anatomy using medical images and radiology reports in a large-scale medical image database
Shuhei Honda, Yoshito Otake (NAIST), Masaki Takao (Osaka Univ.), Eiji Aramaki, Shuntaro Yada, Yuta Hiasa (NAIST), Kento Aida, Shinichi Sato (NII), Akihiro Nishie (Kyushu Univ.), Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) MI2019-69
Recently, the environment for the analysis of large databases, such as the large-scale medical image database, have been... [more] MI2019-69
pp.19-22
NC, MBE
(Joint)
2019-03-06
16:15
Tokyo University of Electro Communications (Tokyo) Examination of Super Resolution and Noise Removal for MicroCT Image
Miku Mashimo, Hayaru Shouno (UEC) NC2018-86
The purpose of this research is to increase the resolution of MicroCT (Computed Tomography) images.
The MicroCT image i... [more]
NC2018-86
pp.227-232
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