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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
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Committee Date Time Place Paper Title / Authors Abstract Paper #
IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] 2022-02-21
15:35		 Online Online Liver Tumor Segmentation by Using a Massive-Training Artificial Neural Network (MTANN) and its Analysis in Liver CT.
Yuqiao Yang, Muneyuki Sato, Ze Jin, Kenji Suzuki (Tokyo Tech) ITS2021-33 IE2021-42		 Based on a 3D massive-training artificial neural network (MTANN) combined with a Hessian-based ellipse enhancer, a small... [more] ITS2021-33 IE2021-42
pp.49-54
IE 2022-01-24
13:05		 Tokyo National Institute of Informatics
(Primary: On-site, Secondary: Online)		 Reduction of Truncation Artifacts by Massive-Training Artificial Neural Network (MTANN) in Fast-Acquisition MRI of the Knee
Maodong Xiang, Ze Jin, Kenji Suzuki (Tokyo Tech) IE2021-31		 MRI has a relatively long acquisition time, leading to patient comfort problems and artifacts from patient motion. Accel... [more] IE2021-31
pp.21-26
IE 2022-01-24
13:30		 Tokyo National Institute of Informatics
(Primary: On-site, Secondary: Online)		 Toward Generating Virtual-High-Dose Breast Tomosynthesis Images from Low-Dose Images Using MTANN
Fahad Parvez Mahdi, Yuto Onai, Ze Jin, Kenji Suzuki (Tokyo Tech) IE2021-32		  [more] IE2021-32
pp.27-32
MI 2020-01-30
14:50		 Okinawa OKINAWAKEN SEINENKAIKAN MR Imaging biomarkers for Prediction of Genetic Assessment for Breast Cancer Recurrence: A Radiogenomics Study
Taiguang Yuan, Ze Jin (Tokyo Tech), Yukiko Tokuda, Noriyuki Tomiyama, Yasuto Naoi (OU), Kenji Suzuki (Tokyo Tech) MI2019-118		  [more] MI2019-118
pp.227-230
MI 2016-01-20
13:05		 Okinawa Bunka Tenbusu Kan [Poster Presentation] A Novel Ellipsoid Convex Enhancement Filter for Boosting the Performance in Detection of Asymptomatic Intracranial Aneurysms at 3.0 T Magnetic Resonance Angiography
Ze Jin, Hidetaka Arimura (Kyushu Univ.), Shingo Kakeda (Univ. Occupational and Environmental Health), Fumio Yamashita, Makoto Sasaki (Iwate Medical Univ.), Yukunori Korogi (Univ. Occupational and Environmental Health) MI2015-126		  [more] MI2015-126
pp.257-261
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