The DOI matches its Crossref record; 233 of the 233 references it cites were found in a registry or an index.
Machine learning and radiology
Open accessAbstract
In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers.
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Cited by
Lakhani, P., & Sundaram, B. (2017) · Radiology · doi:10.1148/radiol.2017162326 · Cited by 1,775
A survey of MRI-based medical image analysis for brain tumor studies
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Metadata
- Authors
- Wang, S., & Summers, R. M.
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2012-07
- Volume
- 16
- Issue
- 5
- Pages
- 933-951
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2069816479
- Cited by
- 697
- References
- 233
- FWCI
- 13.07
- Type
- journal article
- Language
- en
- Open access
- green
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