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Clinical Machine Learning in Action: CAD System Design, Development, Tuning, and Long-Term Experience

Clinical Machine Learning in Action: CAD System Design, Development, Tuning, and Long-Term Experience

Yoshitaka Masutani, Mitsutaka Nemoto, Yukihiro Nomura, Naoto Hayashi
Copyright: © 2012 |Pages: 18
ISBN13: 9781466600591|ISBN10: 1466600594|EISBN13: 9781466600607
DOI: 10.4018/978-1-4666-0059-1.ch008
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MLA

Masutani, Yoshitaka, et al. "Clinical Machine Learning in Action: CAD System Design, Development, Tuning, and Long-Term Experience." Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, edited by Kenji Suzuki, IGI Global, 2012, pp. 159-176. https://doi.org/10.4018/978-1-4666-0059-1.ch008

APA

Masutani, Y., Nemoto, M., Nomura, Y., & Hayashi, N. (2012). Clinical Machine Learning in Action: CAD System Design, Development, Tuning, and Long-Term Experience. In K. Suzuki (Ed.), Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis (pp. 159-176). IGI Global. https://doi.org/10.4018/978-1-4666-0059-1.ch008

Chicago

Masutani, Yoshitaka, et al. "Clinical Machine Learning in Action: CAD System Design, Development, Tuning, and Long-Term Experience." In Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, edited by Kenji Suzuki, 159-176. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0059-1.ch008

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Abstract

This chapter first discusses the database problems in CAD development comprehensively. Then, it introduces the authors’ integrated platform, called the Clinical Infrastructure for Radiologic Computation of United Solutions (CIRCUS), for in-hospital research, development, use, and evaluation of clinical image processing. Based on the authors’ clinical experience and the data collected through the CIRCUS system, they present research results on the improvement of CAD performance as well as simulated studies for additional learning. Finally, the authors’ future plans, including radiologist-CAD collaboration beyond machine learning, are also discussed.

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