T
Toru Tamaki
Researcher at Hiroshima University
Publications - 184
Citations - 1767
Toru Tamaki is an academic researcher from Hiroshima University. The author has contributed to research in topics: Pose & Computer science. The author has an hindex of 18, co-authored 169 publications receiving 1477 citations. Previous affiliations of Toru Tamaki include Nagoya University & Niigata University.
Papers
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Journal ArticleDOI
Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy
Yoko Kominami,Shigeto Yoshida,Shinji Tanaka,Yoji Sanomura,Tsubasa Hirakawa,Bisser Raytchev,Toru Tamaki,Tetsusi Koide,Kazufumi Kaneda,Kazuaki Chayama +9 more
TL;DR: In this article, a real-time image recognition system was used to predict histologic diagnoses of colorectal lesions depicted on narrow-band imaging and to satisfy some problems with the PIVI recommendations.
Iconographies supplémentaires de l'article : Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy
Yoko Kominami,Shigeto Yoshida,Shinji Tanaka,Yoji Sanomura,Tsubasa Hirakawa,Bisser Raytchev,Toru Tamaki,Tetsusi Koide,Kazufumi Kaneda,Kazuaki Chayama +9 more
TL;DR: Although further investigation is necessary to establish the computer-aided diagnosis system, this real-time image recognition system may satisfy the PIVI recommendations and be useful for predicting the histology of colorectal tumors.
Posted Content
Computer-Aided Colorectal Tumor Classification in NBI Endoscopy Using CNN Features
Toru Tamaki,Shoji Sonoyama,Tsubasa Hirakawa,Bisser Raytchev,Kazufumi Kaneda,Tetsushi Koide,Shigeto Yoshida,Hiroshi Mieno,Shinji Tanaka +8 more
TL;DR: A recognition system for classifying NBI images of colorectal tumors into three types (A, B, and C3) based on the NBI magnification findings achieves a recognition rate of 96% for 10-fold cross validation on a real dataset of 908 N BI images collected during actual colonoscopy, and 93% for a separate test dataset.
Journal ArticleDOI
Computer-aided colorectal tumor classification in NBI endoscopy using local features.
Toru Tamaki,Junki Yoshimuta,Misato Kawakami,Bisser Raytchev,Kazufumi Kaneda,Shigeto Yoshida,Yoshito Takemura,Keiichi Onji,Rie Miyaki,Shinji Tanaka +9 more
TL;DR: In this article, a bag-of-visual-words (BoW) representation of local features was used to classify NBI images of colorectal tumors into three types (A, B, and C3) based on the NBI magnification findings.
Journal ArticleDOI
Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video)
Yoshito Takemura,Shigeto Yoshida,Shinji Tanaka,Rie Kawase,Keiichi Onji,Shiro Oka,Toru Tamaki,Bisser Raytchev,Kazufumi Kaneda,Masaharu Yoshihara,Kazuaki Chayama +10 more
TL;DR: A new computer-aided system is reliable for predicting the histology of colorectal tumors by using NBI magnifying colonoscopy by evaluating the utility and limitations of this automated NBI classification system.