K
Kunio Doi
Researcher at University of Chicago
Publications - 584
Citations - 29367
Kunio Doi is an academic researcher from University of Chicago. The author has contributed to research in topics: Computer-aided diagnosis & Image processing. The author has an hindex of 92, co-authored 582 publications receiving 27984 citations. Previous affiliations of Kunio Doi include University of Illinois at Chicago & University of California, Berkeley.
Papers
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Journal ArticleDOI
Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential
TL;DR: The motivation and philosophy for early development of CAD schemes are presented together with the current status and future potential of CAD in a PACS environment.
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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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Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.
TL;DR: It is concluded that three-layer, feed-forward neural networks with a back-propagation algorithm trained for the interpretation of mammograms on the basis of features extracted from mammograms by experienced radiologists may provide a potentially useful tool in the mammographic decision-making task of distinguishing between benign and malignant lesions.
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Current status and future potential of computer-aided diagnosis in medical imaging.
TL;DR: A number of CAD schemes are presented, with emphasis on potential clinical applications, including detection and classification of lung nodules on digital chest radiographs and quantitative analysis of diffuse lung diseases on high resolution CT.