H
Hiroshi Fujita
Researcher at Gifu University
Publications - 760
Citations - 16522
Hiroshi Fujita is an academic researcher from Gifu University. The author has contributed to research in topics: Fundus (eye) & Mammography. The author has an hindex of 54, co-authored 732 publications receiving 14346 citations. Previous affiliations of Hiroshi Fujita include University of Illinois at Chicago & University of Chicago.
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Journal ArticleDOI
Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules.
Junji Shiraishi,Shigehiko Katsuragawa,Junpei Ikezoe,Tsuneo Matsumoto,Kobayashi Takeshi,Ken Ichi Komatsu,Mitate Matsui,Hiroshi Fujita,Yoshie Kodera,Kunio Doi +9 more
TL;DR: A digital image database of chest radiographs with and without a lung nodule was developed and showed that this database can be useful for many purposes, including research, education, quality assurance, and other demonstrations.
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A simple method for determining the modulation transfer function in digital radiography
TL;DR: It is shown that the technique of multiple slit exposure and exponential extrapolation of the LSF tail, which has been commonly used in analog seven-film systems, can be employed in DR systems.
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Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique
TL;DR: The authors' present results show that their scheme can be regarded as a technique for CAD systems to detect nodules in helical CT pulmonary images.
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Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs
Meindert Niemeijer,Bram van Ginneken,Michael J. Cree,Atsushi Mizutani,Gwenole Quellec,Clara I. Sánchez,Bob Zhang,Roberto Hornero,Mathieu Lamard,Chisako Muramatsu,Xiangqian Wu,Guy Cazuguel,Jane You,Agustín Mayo,Qin Li,Yuji Hatanaka,Béatrice Cochener,Christian Roux,Fakhri Karray,María García,Hiroshi Fujita,Michael D. Abràmoff +21 more
TL;DR: The overall results show that microaneurysm detection is a challenging task for both the automatic methods as well as the human expert, and there is room for improvement as the best performing system does not reach the performance of thehuman expert.