Deep Learning in Medical Image Analysis
Deep Learning in Medical Image Analysis
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DOI：
10.1007/978-3-030-33128-3_1
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发表时间：
2020-01-01
期刊：
影响因子：
--
通讯作者：
Zhou, Chuan
中科院分区：
文献类型：
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作者：
Chan, Heang-Ping;Samala, Ravi K.;Zhou, Chuan
深度学习是最先进的机器学习方法。深度学习在许多模式识别应用中的成功带来了兴奋和高度期望，即深度学习或人工智能（AI）可以为医疗保健带来革命性的变化。应用于病变检测或分类的深度学习的早期研究报告了与传统技术相比的上级性能，甚至在某些任务中优于放射科医生。将基于深度学习的医学图像分析应用于计算机辅助诊断（CAD），从而为临床医生提供决策支持并提高各种诊断和治疗过程的准确性和效率的潜力，刺激了CAD的新研发工作。尽管机器学习的新时代令人乐观，但临床实践中CAD或AI工具的开发和实施面临许多挑战。在本章中，我们将讨论其中一些问题以及开发强大的基于深度学习的CAD工具并将这些工具集成到临床工作流程中所需的努力，从而朝着为患者护理提供可靠的智能辅助工具的目标迈进。
Deep learning is the state-of-the-art machine learning approach. The success of deep learning in many pattern recognition applications has brought excitement and high expectations that deep learning, or artificial intelligence (AI), can bring revolutionary changes in health care. Early studies of deep learning applied to lesion detection or classification have reported superior performance compared to those by conventional techniques or even better than radiologists in some tasks. The potential of applying deep-learning-based medical image analysis to computer-aided diagnosis (CAD), thus providing decision support to clinicians and improving the accuracy and efficiency of various diagnostic and treatment processes, has spurred new research and development efforts in CAD. Despite the optimism in this new era of machine learning, the development and implementation of CAD or AI tools in clinical practice face many challenges. In this chapter, we will discuss some of these issues and efforts needed to develop robust deep-learning-based CAD tools and integrate these tools into the clinical workflow, thereby advancing towards the goal of providing reliable intelligent aids for patient care.