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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 6 of 6  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
MI 2023-03-06
09:57
Okinawa OKINAWA SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Multi-label multi-class estimation of pathology in high-resolution chest CT images using SRGAN
Tetsuya Asakawa, Riku Tsuneda, Yuki Sugimoto (TUT), Kazuki Shimizu, Takuyuki Komoda (THC), Masaki Aono (TUT)
 [more]
MI 2023-03-06
15:47
Okinawa OKINAWA SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Predicting disease from Chest-X-ray and Heart-CT images using Fine Tuning
Yuya Kumagai, Hiroki Shinoda, Tetsuya Asakawa (TUT), Kazuki Shimizu, Takuyuki Komoda (THC), Masaki Aono (TUT)
 [more]
PRMU 2022-10-21
16:25
Tokyo Miraikan - The National Museum of Emerging Science and Innovation
(Primary: On-site, Secondary: Online)
Heart Detection Method in Non-Contrast Chest CT Image and Application to Calcium Scoring
Yuki Sugimoto, Tetsuya Asakawa (TUT), Kazuki Shimizu, Takuyuki Komoda (THC), Masaki Aono (TUT) PRMU2022-31
Coronary artery disease, one of the major causes of death in Japan, is said to be related to coronary artery calcificati... [more] PRMU2022-31
pp.50-55
MI 2022-07-09
15:20
Hokkaido
(Primary: On-site, Secondary: Online)
Disease segmentation of 3D diagnostic images -- Disease detection using CT data --
Tetsuya Asakawa, Riku Tsuneda, Yuki Sugimoto (TUT), Kazuki Shimizu, Takuyuki Komoda (THC), Masaki Aono (TUT) MI2022-47
Disease detection from medical images reinforce the judgment of doctors and medical engineers, and is expected to play a... [more] MI2022-47
pp.56-60
PRMU, IPSJ-CVIM 2022-03-10
10:40
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
MI 2021-07-09
14:00
Online Online Severity determination of chest CT data in tuberculosis patients using deep learning
Tetsuya Asakawa, Riku Tsuneda (TUT), Kazuki Simizu, Takuyuki Komoda (THC), Masaki Aono (TUT) MI2021-19
The purpose of this study is to make accurate estimates for five labels (infiltrative, focal, tuberculoma, miliary, and ... [more] MI2021-19
pp.42-46
 Results 1 - 6 of 6  /   
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