Výsledky vyhledávání - "Hiroshi Mieno"

Akademický článek

Anti-spike protein antibody responses to BNT162b2 mRNA vaccine: A single-center survey in a COVID-19 non-epidemic area in Japan

Autor: Sho Mokuda, Namiki Kawanishi, Shintaro Kuroshima, Junko Kono, Hirofumi Nakayama, Hiroshi Mieno, Masashi Kawamoto

Publikováno v: Vaccine: X, Vol 11, Iss , Pp 100173- (2022)

Background: There are a few reports on antibody responses after a two-dose BNT162b2 vaccination in non-epidemic areas. We evaluated this phenomenon. Methods: A total of 344 healthcare workers were vaccinated, and the serum anti-receptor-binding domai

Externí odkaz: https://doaj.org/article/7ea866c6141144f18c0ddce4738cc64a

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Anti-spike protein antibody responses to BNT162b2 mRNA vaccine: A single-center survey in a COVID-19 non-epidemic area in Japan

Autor: Sho Mokuda, Namiki Kawanishi, Shintaro Kuroshima, Junko Kono, Hirofumi Nakayama, Hiroshi Mieno, Masashi Kawamoto

Publikováno v: Vaccine: X. 11

There are a few reports on antibody responses after a two-dose BNT162b2 vaccination in non-epidemic areas. We evaluated this phenomenon.A total of 344 healthcare workers were vaccinated, and the serum anti-receptor-binding domain (RBD) antibody conce

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6cc72d97ed2f274a0f0c2a68f0ad3ac6
https://pubmed.ncbi.nlm.nih.gov/35692460

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A Lesion Classification Method Using Deep Learning Based on NICE Classification for Computer-Aided Diagnosis System in Colorectal NBI Endoscopy

Autor: Yuki Okamoto, Yongfei Wu, Shigeto Yoshida, Seiji Izakura, Shinji Tanaka, Daisuke Katayama, Ryuichi Michida, Hiroshi Mieno, Tetsushi Koide, Toru Tamaki

Publikováno v: 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC).

In this study, we develop and evaluate a colorectal endoscopic diagnosis support system using deep learning. We use image dataset about the JNET (The Japan NBI Expert Team) classification based on the findings classification of the colorectal magnifi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5d0c7d66677a198734342447f5b0f2fe
https://doi.org/10.1109/itc-cscc52171.2021.9501264

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Classification Method with CNN features and SVM for Computer-Aided Diagnosis System in Colorectal Magnified NBI Endoscopy

Autor: Shigeto Yoshida, Tetsushi Koide, Takumi Okamoto, Bisser Raytchev, Shinji Tanaka, Toru Tamaki, Hiroshi Mieno, Kazufumi Kaneda, Masayuki Odagawa

Publikováno v: TENCON

This paper presents a classification method for a Computer-Aided Diagnosis (CAD) system in a colorectal magnified Narrow Band Imaging (NBI) endoscopy. For the classification of a histologic findings, we consider an output result of a lesion endoscopi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2f36bee7d62bc5ec8e627643b2ea213d
https://doi.org/10.1109/tencon50793.2020.9293709

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A Hardware Implementation of Colorectal Tumor Classification for Endoscopic Video on Customizable DSP Toward Real-Time Computer-Aided Diagnosis System

Autor: Hiroshi Mieno, Kazufumi Kaneda, Masayuki Odagawa, Toru Tamaki, Shigeto Yoshida, Shinji Tanaka, Bisser Raytchev, Masayuki Tsuji, Hiroshi Toishi, Tetsushi Koide, Takumi Okamoto, Takayuki Sugawara, Nobuo Tamba

Publikováno v: ISCAS

This paper presents a hardware implementation of a computer-aided diagnosis system that can classify lesions in real time for endoscopic video image. Unlike still images, video images contain many color shifts, blurs, reflections, and so on, a classi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::20760543ef6424dcd833d72157a9e7b1
https://doi.org/10.1109/iscas.2019.8702379

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Feature Extraction of Colorectal Endoscopic Images for Computer-Aided Diagnosis with CNN

Autor: Shigeto Yoshida, Tetsushi Koide, Shinji Tanaka, Takumi Okamoto, Kazufumi Kaneda, Masayuki Odagawa, Hiroshi Mieno, Toru Tamaki, Bisser Raytchev

Publikováno v: ISDCS

This paper introduces a feature extraction method for Narrow-Band Imaging (NBI) colorectal endoscopic images with Convolutional Neural Network (CNN) for Support Vector Machine (SVM) as a Computer-Aided Diagnosis (CAD) system. The proposed method usin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a2e66e9a3067b1c7b7107bd34776b09f
https://doi.org/10.1109/isdcs.2019.8719104

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