Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Shu, Nishida"'
Publikováno v:
BMC Research Notes, Vol 15, Iss 1, Pp 1-5 (2022)
Abstract Objective Mandarina is an endangered land snail genus of the oceanic Ogasawara archipelago. On Chichijima Island, the largest inhabited island in Ogasawara, this genus is almost extinct in the wild due to predation by invasive species. Altho
Externí odkaz:
https://doaj.org/article/56d767582cd540e0a7d4bd9b4af6e141
Autor:
Hiroyuki, Abe, Yusuke, Kurose, Shusuke, Takahama, Ayako, Kume, Shu, Nishida, Miyako, Fukasawa, Yoichi, Yasunaga, Tetsuo, Ushiku, Youichiro, Ninomiya, Akihiko, Yoshizawa, Kohei, Murao, Shin'ichi, Sato, Masaru, Kitsuregawa, Tatsuya, Harada, Masanobu, Kitagawa, Masashi, Fukayama
Publikováno v:
Cancer Science. 113:3608-3617
To overcome the increasing burden on pathologists in diagnosing gastric biopsies, we developed an artificial intelligence-based system for the pathological diagnosis of gastric biopsies (AI-G), which is expected to work well in daily clinical practic
Autor:
Minoru Chiba, Takahiro Hirano, Daishi Yamazaki, Bin Ye, Shun Ito, Osamu Kagawa, Komei Endo, Shu Nishida, Seiji Hara, Kenichiro Aratake, Satoshi Chiba
Publikováno v:
PNAS Nexus. 1
Knowing how the present distribution of organisms was formed is an essential issue in evolutionary ecology. Recently, the distribution of organisms on Earth has been significantly changed by human-mediated dispersal due to globalization. Therefore, s
Autor:
Daisuke Komura, Akihiro Kawabe, Keisuke Fukuta, Kyohei Sano, Toshikazu Umezaki, Hirotomo Koda, Ryohei Suzuki, Ken Tominaga, Mieko Ochi, Hiroki Konishi, Fumiya Masakado, Noriyuki Saito, Yasuyoshi Sato, Takumi Onoyama, Shu Nishida, Genta Furuya, Hiroto Katoh, Hiroharu Yamashita, Kazuhiro Kakimi, Yasuyuki Seto, Tetsuo Ushiku, Masashi Fukayama, Shumpei Ishikawa
Publikováno v:
Cell Reports. 38:110424
Cancer histological images contain rich biological and clinical information, but quantitative representation can be problematic and has prevented the direct comparison and accumulation of large-scale datasets. Here, we show successful universal encod
Autor:
Keisuke Fukuta, Genta Furuya, Hiroto Katoh, Toshikazu Umezaki, Shumpei Ishikawa, Tetsuo Ushiku, Shu Nishida, Kyohei Sano, Hirotomo Koda, Hiroki Konishi, Daisuke Komura, Masahi Fukayama, Ryohei Suzuki, Ken Tominaga, Akihiro Kawabe
SummaryCancer histological images contain rich biological and clinical information, but quantitative representation can be problematic and has prevented direct comparison and accumulation of large-scale datasets. Here we show that deep texture repres
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::81e39de8677e73f0bf7ce9258e655493
https://doi.org/10.1101/2020.07.28.224253
https://doi.org/10.1101/2020.07.28.224253
Publikováno v:
The Proceedings of Conference of Kanto Branch. :473-474