Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Ryoma Bise"'
Autor:
Kosuke Tomimatsu, Takeru Fujii, Ryoma Bise, Kazufumi Hosoda, Yosuke Taniguchi, Hiroshi Ochiai, Hiroaki Ohishi, Kanta Ando, Ryoma Minami, Kaori Tanaka, Taro Tachibana, Seiichi Mori, Akihito Harada, Kazumitsu Maehara, Masao Nagasaki, Seiichi Uchida, Hiroshi Kimura, Masashi Narita, Yasuyuki Ohkawa
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-16 (2024)
Abstract Cell states are regulated by the response of signaling pathways to receptor ligand-binding and intercellular interactions. High-resolution imaging has been attempted to explore the dynamics of these processes and, recently, multiplexed imagi
Externí odkaz:
https://doaj.org/article/00c3f2c5cdf74c7aaf8a2b02b7eb980b
Autor:
Kengo Kawaguchi, Kazuki Miyama, Makoto Endo, Ryoma Bise, Kenichi Kohashi, Takeshi Hirose, Akira Nabeshima, Toshifumi Fujiwara, Yoshihiro Matsumoto, Yoshinao Oda, Yasuharu Nakashima
Publikováno v:
npj Precision Oncology, Vol 8, Iss 1, Pp 1-9 (2024)
Abstract Prognosis after neoadjuvant chemotherapy (NAC) for osteosarcoma is generally predicted using manual necrosis-rate assessments; however, necrosis rates obtained in these assessments are not reproducible and do not adequately reflect individua
Externí odkaz:
https://doaj.org/article/25bb4232a0a8410283873adc71cc00df
Autor:
Kazuki Miyama, Ryoma Bise, Satoshi Ikemura, Kazuhiro Kai, Masaya Kanahori, Shinkichi Arisumi, Taisuke Uchida, Yasuharu Nakashima, Seiichi Uchida
Publikováno v:
Arthritis Research & Therapy, Vol 24, Iss 1, Pp 1-14 (2022)
Abstract Background X-ray images are commonly used to assess the bone destruction of rheumatoid arthritis. The purpose of this study is to propose an automatic-bone-destruction-evaluation system fully utilizing deep neural networks (DNN). This system
Externí odkaz:
https://doaj.org/article/b78274247bb24d358ca8caef4928f57a
Autor:
Takeaki Kadota, Kentaro Abe, Ryoma Bise, Takuji Kawamura, Naokuni Sakiyama, Kiyohito Tanaka, Seiichi Uchida
Publikováno v:
IEEE Access, Vol 10, Pp 25688-25695 (2022)
For automatic disease-severity-level estimation, a large-scale medical image dataset with level annotations is generally necessary. However, attaching absolute-level annotations (such as levels 0, 1, and 3) is very costly and even inaccurate due to t
Externí odkaz:
https://doaj.org/article/615050f86a9a4337981567737651617f
Autor:
Maho Ueda, Susumu Saito, Teruasa Murata, Tomoko Hirano, Ryoma Bise, Kenji Kabashima, Shigehiko Suzuki
Publikováno v:
Scientific Reports, Vol 9, Iss 1, Pp 1-12 (2019)
Abstract The geometric organization of collagen fibers in human reticular dermis and its relationship to that of elastic fibers remain unclear. The tight packing and complex intertwining of dermal collagen fibers hinder accurate analysis of fiber ori
Externí odkaz:
https://doaj.org/article/d7ee556f5cd84117892e556125193208
Autor:
Dai Fei Elmer Ker, Lee E Weiss, Silvina N Junkers, Mei Chen, Zhaozheng Yin, Michael F Sandbothe, Seung-il Huh, Sungeun Eom, Ryoma Bise, Elvira Osuna-Highley, Takeo Kanade, Phil G Campbell
Publikováno v:
PLoS ONE, Vol 6, Iss 11, p e27672 (2011)
Current cell culture practices are dependent upon human operators and remain laborious and highly subjective, resulting in large variations and inconsistent outcomes, especially when using visual assessments of cell confluency to determine the approp
Externí odkaz:
https://doaj.org/article/05b9968acb6d403faebdf13d1404340d
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Cell detection is an important task in biomedical research. Recently, deep learning methods have made it possible to improve the performance of cell detection. However, a detection network trained with training data under a specific condition (source
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e27b8536c26acc9b37bbbb064e356e4
Autor:
Kazuya Nishimura, Ryoma Bise
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).