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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
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Author(s): Yoshitaka Masutani (The University of Tokyo, Japan), Mitsutaka Nemoto (The University of Tokyo, Japan), Yukihiro Nomura (The University of Tokyo, Japan) and Naoto Hayashi (The University of Tokyo, Japan)
Copyright: 2013
Pages: 18
Source title: Image Processing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-3994-2.ch032

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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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