Using deep learning for content-based medical image retrieval

Qinpei Sun; Yuanyuan Yang; Jianyong Sun; Zhiming Yang; Jianguo Zhang

doi:10.1117/12.2251115

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13 March 2017 Using deep learning for content-based medical image retrieval

Qinpei Sun, Yuanyuan Yang, Jianyong Sun, Zhiming Yang, Jianguo Zhang

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Proceedings Volume 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications; 1013812 (2017) https://doi.org/10.1117/12.2251115
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Content-Based medical image retrieval (CBMIR) is been highly active research area from past few years. The retrieval performance of a CBMIR system crucially depends on the feature representation, which have been extensively studied by researchers for decades. Although a variety of techniques have been proposed, it remains one of the most challenging problems in current CBMIR research, which is mainly due to the well-known “semantic gap” issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human[1]. Recent years have witnessed some important advances of new techniques in machine learning. One important breakthrough technique is known as “deep learning”. Unlike conventional machine learning methods that are often using “shallow” architectures, deep learning mimics the human brain that is organized in a deep architecture and processes information through multiple stages of transformation and representation. This means that we do not need to spend enormous energy to extract features manually. In this presentation, we propose a novel framework which uses deep learning to retrieval the medical image to improve the accuracy and speed of a CBIR in integrated RIS/PACS.

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Qinpei Sun, Yuanyuan Yang, Jianyong Sun, Zhiming Yang, and Jianguo Zhang "Using deep learning for content-based medical image retrieval", Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 1013812 (13 March 2017); https://doi.org/10.1117/12.2251115

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