The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.
Current status and future potential of computer-aided diagnosis in medical imaging
Abstract
Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. The basic concept of CAD is to provide a computer output as a second opinion to assist radiologists' image interpretation by improving the accuracy and consistency of radiological diagnosis and also by reducing the image reading time. In this article, a number of CAD schemes are presented, with emphasis on potential clinical applications. These schemes include: (1) detection and classification of lung nodules on digital chest radiographs; (2) detection of nodules in low dose CT; (3) distinction between benign and malignant nodules on high resolution CT; (4) usefulness of similar images for distinction between benign and malignant lesions; (5) quantitative analysis of diffuse lung diseases on high resolution CT; and (6) detection of intracranial aneurysms in magnetic resonance angiography. Because CAD can be applied to all imaging modalities, all body parts and all kinds of examinations, it is likely that CAD will have a major impact on medical imaging and diagnostic radiology in the 21st century.
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Cites
Early Lung Cancer Action Project: overall design and findings from baseline screening
Henschke, C. I., McCauley, D. I., Yankelevitz, D. F., Naidich, D. P., McGuinness, G., Miettinen, O. S., Libby, D. M., Pasmantier, M. W., Koizumi, J., Altorki, N. K., et al. (1999) · The Lancet · doi:10.1016/s0140-6736(99)06093-6 · Cited by 2,508
Case-Fatality Rates and Functional Outcome After Subarachnoid Hemorrhage: A Systematic Review
Hop, J. W., Rinkel, G. J. E., Algra, A., & van Gijn, J. (1997) · Stroke · doi:10.1161/01.str.28.3.660 · Cited by 1,093
Mass screening for lung cancer with mobile spiral computed tomography scanner
Sone, S., Takashima, S., Li, F., Yang, Z., Honda, T., Maruyama, Y., Hasegawa, M., Yamanda, T., Kubo, K., Hanamura, K., et al. (1998) · The Lancet · doi:10.1016/s0140-6736(97)08229-9 · Cited by 931
Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography.
Kaneko, M., Eguchi, K., Ohmatsu, H., Kakinuma, R., Naruke, T., Suemasu, K., & Moriyama, N. (1996) · Radiology · doi:10.1148/radiology.201.3.8939234 · Cited by 831
Freer, T. W., & Ulissey, M. J. (2001) · Radiology · doi:10.1148/radiol.2203001282 · Cited by 751
Juvela, S., Porras, M., & Poussa, K. (2000) · Journal of Neurosurgery · doi:10.3171/jns.2000.93.3.0379 · Cited by 723
Initial and recurrent bleeding are the major causes of death following subarachnoid hemorrhage.
Broderick, J. P., Brott, T. G., Duldner, J. E., Tomsick, T., & Leach, A. (1994) · Stroke · doi:10.1161/01.str.25.7.1342 · Cited by 714
Lung Cancer Screening with CT: Mayo Clinic Experience
Swensen, S. J., Jett, J. R., Hartman, T. E., Midthun, D. E., Sloan, J. A., Sykes, A.-M., Aughenbaugh, G. L., & Clemens, M. A. (2003) · Radiology · doi:10.1148/radiol.2263020036 · Cited by 665
Potential Contribution of Computer-aided Detection to the Sensitivity of Screening Mammography
Warren Burhenne, L. J., Wood, S. A., D'Orsi, C. J., Feig, S. A., Kopans, D. B., O'Shaughnessy, K. F., Sickles, E. A., Tabar, L., Vyborny, C. J., & Castellino, R. A. (2000) · Radiology · doi:10.1148/radiology.215.2.r00ma15554 · Cited by 554
The detection and management of unruptured intracranial aneurysms
Wardlaw, J. M., & White, P. M. (2000) · Brain · doi:10.1093/brain/123.2.205 · Cited by 543
Cited by
Computer-aided diagnosis in medical imaging: historical review, current status and future potential
Doi, K. (2007) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2007.02.002 · Cited by 1,855
Self-citationRecent advances and clinical applications of deep learning in medical image analysis
Chen, X., Wang, X., Zhang, K., Fung, K.-M., Thai, T. C., Moore, K., Mannel, R. S., Liu, H., Zheng, B., & Qiu, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102444 · Cited by 1,080
Large scale deep learning for computer aided detection of mammographic lesions
Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., den Heeten, A., & Karssemeijer, N. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.07.007 · Cited by 965
Machine learning and radiology
Wang, S., & Summers, R. M. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.02.005 · Cited by 697
Automatic Tuberculosis Screening Using Chest Radiographs
Jaeger, S., Karargyris, A., Candemir, S., Folio, L., Siegelman, J., Callaghan, F., Xue, Z., Palaniappan, K., Singh, R. K., Antani, S., et al. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2284099 · Cited by 678
Metadata
- Authors
- Doi, K.
- Venue
- The British Journal of Radiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-01
- Volume
- 78
- Issue
- suppl_1
- Pages
- s3-s19
- ISSN
- 0007-1285 (ISSN-L)
- 1748-880X
- OpenAlex
- W2107166114
- Cited by
- 444
- References
- 96
- FWCI
- 26.80
- Type
- journal article
- Language
- en
- Open access
- closed
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