Computer-aided detection of clustered microcalcifications on digital mammograms-Reference-Cited by-同舟云学术

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Computer-aided detection of clustered microcalcifications on digital mammograms

Published:1995-03 Issue:2 Volume:33 Page:174-178
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med. Biol. Eng. Comput.

Author:

Nishikawa R. M.,Giger M. L.,Doi K.,Vyborny C. J.,Schmidt R. A.

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

http://link.springer.com/content/pdf/10.1007/BF02523037.pdf

Reference24 articles.

1. Andersson, I., Aspegren, K., Janzon, L., Landberg, T., Lindholm, K., Linell, F., Ljungberg, O., Jonas, R., andSigfusson, B. (1988): ‘Mammographic screening and mortality from breast cancer: the Malmo mammographic screening trial’,Br. Med. J.,297, pp. 943–948

2. Astley, S. M., Taylor, C. J., Boggis, C. R. M., Wison, M., andEllison, T. P. (1990): ‘Automated detection of abnormalities on screening mammograms’,Radiology,177(P), p. 288 (abstract)

3. Baines, C. J., Miller, A. B., Wall, C., McFarlane, D. V., Simor, I. S., Jong, R., Shapiro, B. J., Audet, L., Petitclerc, M., Ouimet-Oliva, D., Ladouceur, J., Hebert, G., Minuk, T., Hardy, G., andStanding, H. K. (1986): ‘Sensitivity and specificity of first screen mammography in the Canadian national breast screening study: A preliminary report from five centers,’ibid.,160, pp. 295–298

4. Bird, R. E. (1989): ‘Professional quality assurance for mammography screening programs’,ibid.,177, p. 587

5. Chan, H.-P., Doi, K., Galhotra, S., Vyborny, C. J., MacMahon, H., andJokich, P. M. (1987): ‘Image feature analysis and computer-aided diagnosis in digital radiography. 1. Automated detection of microcalcifications in mammography’,Med. Phys.,14, pp. 538–548

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1. Breast cancer diagnosis through microcalcification;Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images;2024

2. A Review on Diagnosis of Breast Cancer Using Mammography Techniques;Communication and Intelligent Systems;2023

3. Classification of Microcalcification Clusters Using Bilateral Features Based on Graph Convolutional Network;Frontiers in Oncology;2022-05-13

4. Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases;IEEE Transactions on Biomedical Engineering;2021

5. Adaptive Gaussian Mixture Model-Based Statistical Feature Extraction for Computer-Aided Diagnosis of Micro-Calcification Clusters in Mammograms;SICE Journal of Control, Measurement, and System Integration;2020-07-01

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