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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI | 2024-03-04 10:22 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Robust segmentation approach over various training-to-test ratios for gross tumor volumes of lung cancer based on fused outputs Yunhao Cui, Hidetaka Arimura (Kyushu Univ.), Yuko Shirakawa (National Hospital Organization Kyushu Cancer), Tadamasa Yoshitake (Kyushu Univ.), Yoshiyuki Shioyama (Saga HIMAT), Hidetake Yabuuchi (Kyushu Univ.)  MI2023-68 |
This study investigates robust deep learning (DL) methods for segmenting lung cancer from stereotactic body radiotherapy... [more] | MI2023-68 pp.117-118 |
| MI | 2024-03-04 14:48 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Pathological image features for prediction of disease progression of lung cancer patients who received radiation therapy Jin Yu, Hidetaka Arimura, Takeshi Iwasaki, Takumi Kodama, Cui YunHao, Yoshinao Oda (Kyushu Uni.) MI2023-83 |
Radiation therapy is one of treatment options for lung cancer patients with early and late stages. Since the disease pro... [more] | MI2023-83 pp.166-168 |
| MI | 2024-03-04 15:22 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Prediction of Cancer Relapse for Non-Small Cell Lung Cancer Patients Using Clinical and Imaging Features Before Stereotactic Ablative Radiotherapy Takumi Kodama, Hidetaka Arimura (Kyushu Univ.), Yuko Shirakawa (Kyushu Cancer Center), Kenta Ninomiya (Sanford Burnham Prebys Med. Discov. Inst.), Tadamasa Yoshitake (Kyushu Univ.), Yoshiyuki Shioyama (SAGA HIMAT) MI2023-85 |
The aim of this study is to predict locoregional relapse and distant metastasis in stage I non-small cell lung cancer pa... [more] | MI2023-85 pp.173-175 |
| HIP | 2023-12-21 15:15 |
Miyagi | Research Institute of Electrical Communication | Verification of the effect of a music application based on the automatic composition system "soundtope" on improving mood.
-- For the purpose of developing medical applications -- Reiko Shiba (Tokyo Geidai), Yota Morimoto (coton inc.), Ruriko Akimoto (Tokyo Geidai), Kohji Nageishi, Tatsuo Akechi (Nagoya City Univ Grad School Med Sci), Maiko Fujimori, Yosuke Uchitomi (NCC), Kiyoshi Furukawa (Tokyo Geidai) HIP2023-82 |
The purpose of this study is to develop a medical application to improve anxiety and fatigue in cancer patients and canc... [more] | HIP2023-82 pp.31-35 |
| MI, MICT | 2023-11-14 15:00 |
Fukuoka | Pre-training without natural images for Cystoscopic AI Diagnosis of Bladder Cancer Ryuunosuke Kounosu (AIST/Toho Univ.), Wonjik Kim (AIST), Atsushi Ikeda (Univ. of Tsukuba), Hirokazu Nosato (AIST), Yuu Nakajima (Toho Univ.) MICT2023-34 MI2023-27 |
When developing AI models, it is sometimes difficult to collect sufficient training data. In these cases, pre-trained AI... [more] | MICT2023-34 MI2023-27 pp.37-40 |
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| MI | 2023-03-06 09:44 |
Okinawa | OKINAWA SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Machine learning models using CT images and clinical variable to classify tumors in testicular cancer Shota Nakagawa, Masanobu Gido (University of Tsukuba), Satoshi Nitta (University of Tsukuba Hospital), Takahiro Kojima (Aichi Cancer Center Hospital), Hideki Kakeya (University of Tsukuba) MI2022-75 |
This paper presents machine learning methods to classify tumors in testicular cancer. The model combines CT images and c... [more] | MI2022-75 pp.8-13 |
| MI | 2023-03-06 10:10 |
Okinawa | OKINAWA SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Radiogenomic signature based on CT images to predict HOPX gene expression and prognoses of patients with non-small cell lung cancer
-- Prognoses and prediction of HOPX expression for NSCLC based on CT image -- Yu Jin, Hidetaka Arimura, YunHao Cui, Takumi Kodama, Shinichi Mizuno (Kyushu Univ.), Satoshi Ansai (Kyoto Univ.) MI2022-77 |
The homeodomain-only protein homeobox, HOPX, has recently been discovered that associated with the prognoses of non-smal... [more] | MI2022-77 pp.20-23 |
| MI | 2023-03-06 16:00 |
Okinawa | OKINAWA SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Segmentation of renal cancers from multi-phase CT images by deep learning using selective fusion Masanobu Gido (Tsukuba Univ.), Ryo Tanimoto, Kensaku Mori, Hideki Kakeya (Tsukuba Univ.) MI2022-92 |
Multiphase CT images are commonly used for the diagnosis of renal cancer. In this paper, we propose a machine learning s... [more] | MI2022-92 pp.94-99 |
| IA | 2023-01-25 16:40 |
Osaka | Osaka Umeda Campus, Kwansei Gakuin University (Osaka) (Primary: On-site, Secondary: Online) |
Integrating Data Science and Open Data in Prostate Cancer Decision Support Platform Case study on Songklanagarind Hospital Statisticians Nattawut Thongpim (Faculty of Medicine, Prince of Songkla Univ.), Chidchanok Choksuchat, Sorawit Khumnaewnak, Sirima Kanghae (Faculty of Science, Prince of Songkla Univ.), Sureena Matayong (Faculty of Engineering, Prince of Songkla Univ.), Tanan Bejrananda (Faculty of Medicine, Prince of Songkla Univ.), Korakot Wichitsa-nguan Jetwanna (Faculty of Science, Prince of Songkla Univ.) IA2022-75 |
There are an increasing number of new cases’ prostate cancer in Thailand, and highly mortality rates every year. The pur... [more] | IA2022-75 pp.49-53 |
| IMQ | 2022-12-16 14:55 |
Chiba | Nishi-Chiba Campus, Chiba Univ. | Development of a Breast Cancer Surgery Support System with Stable MRI Image Superimposition by Colorization of Fiducial Markers Mizuki Hattori, Takaya Oguchi, Hiroshi Fujimoto (Chiba Univ.), Yoshihiro Kuroda (Univ. of Tsukuba), Katsuhiro Nasu, Yukihiro Nomura, Hideki Hayashi, Toshiya Nakaguchi (Chiba Univ.) IMQ2022-16 |
Magnetic Resonance Imaging (MRI) is used to determine the extent of resection in breast cancer surgery, but tumor resect... [more] | IMQ2022-16 pp.7-11 |
| MI | 2022-09-15 11:25 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Esophageal Tumor Segmentation in Endoscopic Images by Deep Learning Zehao Li, Ken'ichi Morooka (Okayama Univ.), Yuho Ebata (Kyushu Univ.), Hirofumi Hasuda (NHOKMC), Shoko Miyauchi, Ota Mitsuhiko (Kyushu Univ.) MI2022-54 |
Esophageal cancer is often asymptomatic at early stage.It progresses rapidly and can invade surrounding tissues.The esop... [more] | MI2022-54 pp.26-27 |
| MI | 2022-09-15 14:15 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Study of Detecting Oral Disease Using Oral Images Acquired by a Dermoscope and Deep Learning Yuta Suzuki, Jun Ohya (Waseda Univ.), Toshihiro Okamoto, Nobuyuki Kaibuchi, Katsuhisa Sakaguchi, Kitaro Yoshimitsu (TWMU), Eiji Fukuzawa (Waseda U./Yazaki) MI2022-58 |
In this study, we focused on a device called a dermoscope as a simple and minimally invasive diagnostic tool instead of ... [more] | MI2022-58 pp.39-44 |
| PRMU, IPSJ-CVIM | 2022-05-13 15:35 |
Aichi | Toyota Technological Institute | Automatic Classification of Cervical Cancer Cells using Deep Learning Yukihiro Tsuboshita, Mitsuaki Okodo (Kyorin Univ.) PRMU2022-9 |
(To be available after the conference date) [more] | PRMU2022-9 pp.47-51 |
| MI | 2022-01-27 13:28 |
Online | Online | Study of Automatic Diagnosis of Early Oral Cancers Based on Fluorescence Images by 5-ALA and Deep Learning
-- Automatic Generation of Fluorescence Images Using GAN and Classification of Stages by CNN -- Taro Fujimoto (Waseda Univ.), Eiji Fukuzawa (Waseda Univ./Yazaki), Seiko Tatehara, Kazuhito Satomura (Tsurumi Univ.), Jun Ohya (Waseda Univ.) MI2021-76 |
A screening system for early-stage oral cancers should be established because detecting them is difficult even for speci... [more] | MI2021-76 pp.135-140 |
| WPT | 2021-12-09 13:30 |
Online | Online | A basic experiment with cancer cells for photodynamic therapy by wireless power transfer Yoshitaka Yasuda, Takehiro Imura, Yoichi Hori, Kenta Yokoi, Azusa Kanbe, Masaki Kakihana, Shin Aoki (TUS) WPT2021-14 |
Recently, Photodynamic Therapy (PDT) using light has been proposed as a cancer treatment method, and it is expected to b... [more] | WPT2021-14 pp.5-8 |
| MI, MICT [detail] | 2021-11-05 11:05 |
Online | Online | [Short Paper]
Description of microvessel structures in 3D reconstructed microscopic pathological images of pancreatic cancer Yuka Ishimaki, Tatsuya Yokota, Kugler Mauricio (NITech), Kenoki Ohuchida (KU), Hidekata Hontani (NITech) MICT2021-33 MI2021-31 |
In this manuscript, we propose a method that segments microvascular regions in a 3D pathological image. For this purpose... [more] | MICT2021-33 MI2021-31 pp.26-27 |
| MI, MICT [detail] | 2021-11-05 15:50 |
Online | Online | [Short Paper]
Sketch-based CT image generation of lung cancers using Pix2pix
-- An attempt to improve representation by adopting Style Blocks -- Ryo Toda, Atsushi Teramoto (FHU), Masakazu Tsujimoto (FHUH), Hiroshi Toyama, Masashi Kondo, Kazuyoshi Imaizumi, Kuniaki Saito (FHU), Hiroshi Fujita (Gifu Univ.) MICT2021-42 MI2021-40 |
Generative adversarial networks (GAN) have been used to overcome the lack of data in medical images. However, such appli... [more] | MICT2021-42 MI2021-40 pp.66-67 |
| MBE, NC (Joint) |
2021-10-29 09:40 |
Online | Online | Computer simulations of colorectal tumor development Riku Ichinohe (Tohoku Univ.), Norihiro Katayama (Tohoku Univ./Shokei Univ.), Mitsuyuki Nakao (Tohoku Univ.) MBE2021-23 |
It is known that prevalence of colorectal cancer increases in work schedules that do not follow the circadian rhythm suc... [more] | MBE2021-23 pp.32-36 |
| MI | 2021-07-08 15:00 |
Online | Online | [Short Paper]
Renal tumor analysis using multi-temporal abdominal CT images Kento Nishihira, Hidenobu Suzuki, Mikio Matsuhiro, Yoshiki Kawata, Noboru Niki (Tokushima Univ.), Atsushi ikeda (Tsukuba Univ.) MI2021-13 |
The development of multislice computed tomography CT systems has enabled highly accurate analysis, diagnosis, and treatm... [more] | MI2021-13 pp.20-22 |
| MI | 2021-03-16 17:15 |
Online | Online | Detectability study for detecting micro-calcifications in Breast using Flow Sensitive Black Blood (FSBB) Airi Shimizu (Shin-Yuri Hospital), Kuninori Kobayashi, Shigehide Kuhara, Kazunori Kuroki (Kyorin Univ.) MI2020-85 |
Mammography is the main method for breast cancer screening. However, it involves radiation exposure, and visualization o... [more] | MI2020-85 pp.158-163 |
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