Abstract：
: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.
journal_name
Adv Exp Med Bioljournal_title
Advances in experimental medicine and biologyauthors
Chan HP,Samala RK,Hadjiiski LM,Zhou Cdoi
10.1007/978-3-030-33128-3_1subject
Has Abstractpub_date
2020-01-01 00:00:00pages
3-21eissn
0065-2598issn
2214-8019journal_volume
1213pub_type
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