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Relevance Feedback as New Tool for Computer-Aided Diagnosis in Image Databases

Relevance Feedback as New Tool for Computer-Aided Diagnosis in Image Databases
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Author(s): Issam El Naqa (McGill University, Canada), Jung Hun Oh (Memorial Sloan Kettering Cancer Center, USA)and Yongyi Yang (Illinois Institute of Technology, USA)
Copyright: 2012
Pages: 21
Source title: Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis
Source Author(s)/Editor(s): Kenji Suzuki (University of Chicago, USA)
DOI: 10.4018/978-1-4666-0059-1.ch004

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Abstract

With the ever-growing volume of images used in medicine, the capability to retrieve relevant images from large databases is becoming increasingly important. Despite the recent progress made in the field, its applications in Computer-Aided Diagnosis (CAD) thus far have been limited by the ability to determine the intrinsic mapping between high-level user perception and the underlying low-level image features. Relevance Feedback (RFB) is a post-query process to refine the search by using positive and/or negative indications from the user about the relevance of retrieved images, which has been applied successfully in traditional text-retrieval systems for improving the results of a retrieval strategy. In this chapter, the authors review some recent advances in RFB technology, and discuss its expanding role in content-based image retrieval from medical archives. They provide working examples, based on their experience, for developing machine-learning methods for RFB in mammography and highlight the potential opportunities in this field for CAD applications and clinical decision-making.

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