2000 - Fellow of the Indian National Academy of Engineering (INAE)
Kunio Doi mainly investigates Radiology, Artificial intelligence, Computer-aided diagnosis, Radiography and Medical imaging. His Radiology research includes elements of Receiver operating characteristic analysis, Lung, Nuclear medicine and Receiver operating characteristic. He has researched Artificial intelligence in several fields, including Mammography, Computer vision and Pattern recognition.
In his research on the topic of Computer-aided diagnosis, Second opinion is strongly related with CAD. The concepts of his Radiography study are interwoven with issues in Tomography and Early detection. His study explores the link between Medical imaging and topics such as Digital radiography that cross with problems in Optics and Optical transfer function.
His main research concerns Radiography, Radiology, Artificial intelligence, Computer-aided diagnosis and Computer vision. His Radiography study combines topics from a wide range of disciplines, such as Image quality, Nuclear medicine and Optics. Kunio Doi combines subjects such as Temporal subtraction, Computed radiography and Radiographic Image Enhancement with his study of Nuclear medicine.
Kunio Doi interconnects Differential diagnosis, Lung cancer, Lung and Receiver operating characteristic in the investigation of issues within Radiology. His Artificial intelligence research is multidisciplinary, relying on both Mammography and Pattern recognition. Kunio Doi focuses mostly in the field of Computer-aided diagnosis, narrowing it down to matters related to CAD and, in some cases, Second opinion.
His scientific interests lie mostly in Radiology, Computer-aided diagnosis, Artificial intelligence, Computer vision and Radiography. The study incorporates disciplines such as Differential diagnosis, Lung, Nuclear medicine and Receiver operating characteristic in addition to Radiology. His study in Computer-aided diagnosis is interdisciplinary in nature, drawing from both False positive paradox, Medical imaging, Presentation, CAD and Hemangioma.
His Artificial intelligence study combines topics in areas such as Mammography and Pattern recognition. His studies in Computer vision integrate themes in fields like Temporal subtraction and Input device. His Radiography research integrates issues from Thorax, Computer aided detection and Projection.
His primary areas of study are Radiology, Computer-aided diagnosis, Radiography, Artificial intelligence and Lung. His Radiology research is multidisciplinary, incorporating perspectives in Differential diagnosis, Nuclear medicine and Receiver operating characteristic. His Computer-aided diagnosis research incorporates elements of Medical imaging, Diagnostic accuracy, Breast imaging, CAD and Observer performance.
His biological study spans a wide range of topics, including Generalization, Cross-validation, Monte Carlo method and Econometrics. His Artificial intelligence research includes themes of Mammography, Computer vision and Pattern recognition. The various areas that Kunio Doi examines in his Lung study include False positive paradox, Lateral chest, Thorax, Lung cancer and Dual energy subtraction.
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Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential
Computerized Medical Imaging and Graphics (2007)
Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO, Advanced Virgo and KAGRA
B. P. Abbott;R. Abbott;T. D. Abbott;M. R. Abernathy.
Living Reviews in Relativity (2018)
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.
American Journal of Roentgenology (2000)
A simple method for determining the modulation transfer function in digital radiography
H. Fujita;D.-Y. Tsai;T. Itoh;K. Doi.
IEEE Transactions on Medical Imaging (1992)
Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.
Yuzheng Wu;M. L. Giger;Kunio Doi;C. J. Vyborny.
Current status and future potential of computer-aided diagnosis in medical imaging.
British Journal of Radiology (2005)
Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography
Heang Ping Chan;Kunio Doi;Simranjit Galhotra;Carl J. Vyborny.
Medical Physics (1987)
Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans.
Qiang Li;Shusuke Sone;Kunio Doi.
Medical Physics (2003)
Computerized Detection of Pulmonary Nodules on CT Scans
Samuel G. Armato;Maryellen L. Giger;Catherine J. Moran;James T. Blackburn.
Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis.
H P Chan;K Doi;C J Vyborny;R A Schmidt.
Investigative Radiology (1990)
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