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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 #
MI 2024-03-03
10:40		 Okinawa OKINAWAKEN SEINENKAIKAN (Okinawa, Online)
(Primary: On-site, Secondary: Online)		 [Short Paper] Pilot study of multitask learning for upper abdominal organ extraction and lesion detection on PET/CT images
Kohei Yoshida, Mitsutaka Nemoto, Kazuki Otani (Kindai Univ.), Hayato Kaida (KU Hosp), Yuichi Kimura, Takashi Nagaoka, Katsuhiro Mikami (Kindai Univ.), Takahiro Yamada, Kohei Hanaoka (KU Hosp), Tatsuya Tsuchitani, Kazuhiro Kitajima (HMU Hosp), Kazunari Ishii (KU Hosp) MI2023-37		 As one of the studies for highly accurate detection of adrenal metastatic lesions on FDG-PET/CT images, multi-task learn... [more] MI2023-37
pp.25-27
MI 2024-03-03
11:40		 Okinawa OKINAWAKEN SEINENKAIKAN (Okinawa, Online)
(Primary: On-site, Secondary: Online)		 [Short Paper] Weak supervised chest lesion detection on FDG-PET/CT Images using Pix2Pix image modality translation
Kazuki Otani, Kohei Yoshida, Mitsutaka Nemoto (Kindai Univ.), Hayato Kaida (KU Hosp), Yuichi Kimura, Takashi Nagaoka, Katsuhiro Mikami (Kindai Univ.), Takahiro Yamada, Kohei Hanaoka (KU Hosp), Tsuchitani Tatsuya, Kazuhiro Kitajima (HMU Hosp), Kazunari Ishii (KU Hosp) MI2023-42		 In this study, we propose a method for detecting thoracic lesions on PET/CT images using 3D Pix2Pix, which learns a tran... [more] MI2023-42
pp.36-38
MI 2020-01-29
09:55		 Okinawa OKINAWAKEN SEINENKAIKAN (Okinawa) Automated dection for neck and thoracic lesions on FDG-PET/CT by lesion enhancement using one-class SVM
Atsuko Tanaka, Mitsutaka Memoto, Hayato Kaida, Yuichi Kimura, Takashi Nagaoka, Takahiro Yamada, Kazuyuki Ushifusa (Kindai Uni), Kohei Hanaoka (Kindai Uni Hosp), Kazuhiro Kitajima (Hyogo Col of Med), Tatsuya Tsuchitani (Hosp of Hyogo Col of Med), Kazunari Ishii (Kindai Uni) MI2019-67		 We propose an anomaly detection based method to detect primary and metastatic lesions in the cervical and thoracic regio... [more] MI2019-67
pp.11-14
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