从医学图像中挖掘解剖、生理和病理信息

X. Zhou, Y. Zhan, V. Raykar, G. Hermosillo, L. Bogoni, Zhigang Peng
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引用次数: 5

摘要

在过去的十年里,医学成像领域显示出了实质性的增长。在这个领域中,机器学习和数据挖掘技术的使用甚至出现了更大幅度的增长。在本文中,我们讨论了医学成像领域中与信息挖掘相关的三个方面:目标用户群体(为谁),挖掘的信息(什么)和挖掘的技术(如何)。具体来说,我们关注三种类型的信息:解剖、生理和病理,并为每一种信息提供用例。此外,我们还介绍了有效解决这些问题的代表性方法和算法。最后,我们讨论了未来十年相关领域的一些主要趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining anatomical, physiological and pathological information from medical images
The field of medical imaging has shown substantial growth over the last decade. Even more dramatic increase was observed in the use of machine learning and data mining techniques within this field. In this paper, we discuss three aspects related to information mining in the domain of medical imaging: the target user groups (for whom), the information to mine (what), and technologies to enable mining (how). Specifically, we focus on three types of information: anatomical, physiological and pathological, and present use cases for each one of them. Furthermore, we introduce representative methods and algorithms that are effective for solving these problems. We conclude the paper by discussing some major trends in the related domains for the coming decade.
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