2017 - SPIE Fellow
His main research concerns Mammography, Artificial intelligence, Pattern recognition, Computer-aided diagnosis and Radiology. His work in the fields of Mammography, such as Digital mammography, intersects with other areas such as Sensitivity. His Artificial intelligence research is multidisciplinary, incorporating elements of Microcalcification, Computer vision and Receiver operating characteristic.
His Pattern recognition research includes themes of Pixel, Iterative reconstruction, Feature and Projection. His work in Computer-aided diagnosis addresses subjects such as Histogram, which are connected to disciplines such as False positive rate, Standard deviation, Cluster analysis and Digital imaging. His research integrates issues of Nuclear medicine and Pathology in his study of Radiology.
His scientific interests lie mostly in Artificial intelligence, Mammography, Pattern recognition, Computer-aided diagnosis and Computer vision. His biological study spans a wide range of topics, including Microcalcification, Tomosynthesis and Receiver operating characteristic. His Mammography research is multidisciplinary, relying on both Radiology, Medical imaging and CAD.
Many of his studies on Pattern recognition involve topics that are commonly interrelated, such as Contextual image classification. The various areas that Robert M. Nishikawa examines in his Computer-aided diagnosis study include Image processing, Medical physics, Data mining and Feature. Robert M. Nishikawa usually deals with Digital mammography and limits it to topics linked to Optics and Signal-to-noise ratio.
Robert M. Nishikawa mainly investigates Artificial intelligence, Pattern recognition, Mammography, Breast cancer and Radiology. His Artificial intelligence study frequently links to adjacent areas such as Computer vision. His Pattern recognition research incorporates themes from False positive paradox, Feature and Pathology.
His research on Mammography focuses in particular on Digital mammography. The Breast imaging and Computer aided detection research Robert M. Nishikawa does as part of his general Breast cancer study is frequently linked to other disciplines of science, such as Occult cancer, therefore creating a link between diverse domains of science. His work focuses on many connections between Radiology and other disciplines, such as Medical physics, that overlap with his field of interest in False detection, Clinical trial, Imaging science and Radiological weapon.
His primary areas of investigation include Artificial intelligence, Mammography, Pattern recognition, Computer-aided diagnosis and Radiology. His Artificial intelligence study incorporates themes from Data mining and Computer vision. Digital mammography is the focus of his Mammography research.
Robert M. Nishikawa combines subjects such as Microcalcification and Feature with his study of Pattern recognition. Robert M. Nishikawa regularly ties together related areas like Breast cancer in his Radiology studies. In Classifier, he works on issues like Receiver operating characteristic, which are connected to Detector.
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A support vector machine approach for detection of microcalcifications
I. El-Naqa;Yongyi Yang;M.N. Wernick;N.P. Galatsanos.
IEEE Transactions on Medical Imaging (2002)
Improving breast cancer diagnosis with computer-aided diagnosis
Yulei Jiang;Robert M. Nishikawa;Robert A. Schmidt;Charles E. Metz.
Academic Radiology (1999)
A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications
Liyang Wei;Yongyi Yang;R.M. Nishikawa;Yulei Jiang.
IEEE Transactions on Medical Imaging (2005)
A similarity learning approach to content-based image retrieval: application to digital mammography
I. El-Naqa;Yongyi Yang;N.P. Galatsanos;R.M. Nishikawa.
IEEE Transactions on Medical Imaging (2004)
Malignant and benign clustered microcalcifications: automated feature analysis and classification.
Y Jiang;R M Nishikawa;D E Wolverton;C E Metz.
A receiver operating characteristic partial area index for highly sensitive diagnostic tests
Y Jiang;C E Metz;R M Nishikawa.
Computer-aided diagnosis in radiology: potential and pitfalls
Kunio Doi;Heber MacMahon;Shigehiko Katsuragawa;Robert M Nishikawa.
European Journal of Radiology (1999)
Current status and future directions of computer-aided diagnosis in mammography
Robert M. Nishikawa.
Computerized Medical Imaging and Graphics (2007)
Methods for improving the accuracy in differential diagnosis on radiologic examinations
Robert M. Nishikawa;Yulei Jiang;Kazuto Ashizawa;Kunio Doi.
Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks.
Yuzheng Wu;Kunio Doi;Maryellen L. Giger;Robert M. Nishikawa.
Medical Physics (1992)
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