Using Deep Learning for Content-Based Medical Image Retrieval
文献类型：会议论文
| 作者 | Sun QP; Yang YY; Sun JY; Yang ZM; Zhang JG |
| 出版日期 | 2017 |
| DOI | 10.1117/12.2251115 |
| 英文摘要 | 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. |
| 语种 | 英语 |
| 源URL | [http://202.127.2.71:8080/handle/181331/12109]  |
| 专题 | 上海技术物理研究所_上海技物所 |
| 作者单位 | Shanghai Inst Tech Phys |
| 推荐引用方式 GB/T 7714 | Sun QP,Yang YY,Sun JY,et al. Using Deep Learning for Content-Based Medical Image Retrieval[C]. 见:. |
入库方式： OAI收割
来源：上海技术物理研究所
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