Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Kazuki Yamagata"'
Autor:
Kazuki Yamagata, Masahiro Yanagawa, Akinori Hata, Ryo Ogawa, Noriko Kikuchi, Shuhei Doi, Keisuke Ninomiya, Yukiko Tokuda, Noriyuki Tomiyama
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024)
Abstract We examined the association between texture features using three-dimensional (3D) io-dine density histogram on delayed phase of dual-energy CT (DECT) and expression of programmed death-ligand 1 (PD-L1) using immunostaining methods in non-sma
Externí odkaz:
https://doaj.org/article/f910323114a94c35aa049304248c3efd
Autor:
Junya Sato, Yuki Suzuki, Tomohiro Wataya, Daiki Nishigaki, Kosuke Kita, Kazuki Yamagata, Noriyuki Tomiyama, Shoji Kido
Publikováno v:
iScience, Vol 26, Iss 7, Pp 107086- (2023)
Summary: In this study, we present a self-supervised learning (SSL)-based model that enables anatomical structure-based unsupervised anomaly detection (UAD). The model employs an anatomy-aware pasting (AnatPaste) augmentation tool that uses a thresho
Externí odkaz:
https://doaj.org/article/8c944889635c444b862a97cbf8d5c80a
Autor:
Tomo Miyata, Masahiro Yanagawa, Noriko Kikuchi, Kazuki Yamagata, Yukihisa Sato, Yuriko Yoshida, Mitsuko Tsubamoto, Noriyuki Tomiyama
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-9 (2022)
Abstract To compare the quality of CT images of the lung reconstructed using deep learning-based reconstruction (True Fidelity Image: TFI ™; GE Healthcare) to filtered back projection (FBP), and to determine the minimum tube current–time product
Externí odkaz:
https://doaj.org/article/40668b28426a4cef989bb853f413c9b0
Autor:
Shuhei Doi, Masahiro Yanagawa, Takahiro Matsui, Akinori Hata, Noriko Kikuchi, Yuriko Yoshida, Kazuki Yamagata, Keisuke Ninomiya, Shoji Kido, Noriyuki Tomiyama
Publikováno v:
Journal of Clinical Medicine, Vol 12, Iss 17, p 5610 (2023)
Background: Dual-energy CT has been reported to be useful for differentiating thymic epithelial tumors. The purpose is to evaluate thymic epithelial tumors by using three-dimensional (3D) iodine density histogram texture analysis on dual-energy CT an
Externí odkaz:
https://doaj.org/article/021a7b999e1540f8a12852957ac2b1f3
Autor:
Yuriko Yoshida, Masahiro Yanagawa, Akinori Hata, Yukihisa Sato, Mitsuko Tsubamoto, Shuhei Doi, Kazuki Yamagata, Tomo Miyata, Noriko Kikuchi, Noriyuki Tomiyama
Publikováno v:
European Journal of Radiology Open, Vol 8, Iss , Pp 100362- (2021)
Objectives: To compare high-resolution (HR) and conventional (C) settings of high-spatial-resolution computed tomography (CT) for software volumetry of ground-glass nodules (GGNs) in phantoms and patients. Methods: We placed −800 and −630 HU sphe
Externí odkaz:
https://doaj.org/article/3c9625af701b4f758995ae5f6496e9a2
Publikováno v:
European Radiology. 33:348-359
Autor:
Kazuki Yamagata, Yuki Suzuki, Noriyuki Tomiyama, Junya Sato, Shoji Kido, Daiki Nishigaki, Kosuke Kita, Tomohiro Wataya
Publikováno v:
Radiol Artif Intell
PURPOSE: To assess whether transfer learning with a bidirectional encoder representations from transformers (BERT) model, pretrained on a clinical corpus, can perform sentence-level anatomic classification of free-text radiology reports, even for ana
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3acce2ad060cc80838820a6628df0077
https://europepmc.org/articles/PMC10077075/
https://europepmc.org/articles/PMC10077075/
Publikováno v:
Journal of the American Ceramic Society. 105:2182-2195
Autor:
Tomohiro, Wataya, Masahiro, Yanagawa, Mitsuko, Tsubamoto, Tomoharu, Sato, Daiki, Nishigaki, Kosuke, Kita, Kazuki, Yamagata, Yuki, Suzuki, Akinori, Hata, Shoji, Kido, Noriyuki, Tomiyama
Publikováno v:
European radiology.
To compare the performance of radiologists in characterizing and diagnosing pulmonary nodules/masses with and without deep learning (DL)-based computer-aided diagnosis (CAD).We studied a total of 101 nodules/masses detected on CT performed between Ja
Autor:
Nobuo, Kashiwagi, Mio, Sakai, Akio, Tsukabe, Yuichi, Yamashita, Masahiro, Fujiwara, Kazuki, Yamagata, Atsushi, Nakamoto, Katsuyuki, Nakanishi, Noriyuki, Tomiyama
Publikováno v:
European Journal of Radiology. 156:110531
A major drawback of magnetic resonance imaging (MRI) is its limited imaging speed. This study proposed an ultrafast cervical spine MRI protocol (2 min 57 s) using deep learning-based reconstruction (DLR) and compared the diagnostic results to those o