Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies

 Access to full text

 Full (PDF)

1. Ballard H. (1981). Generalizing the Hough transform to detect arbitrary Shapes, Pattern Recognition 13(2): 111-122. Zbl0454.68112
2. Bloom H. and Richardson W. (1957). Histological grading and prognosis in breast cancer, British Journal of Cancer, 11(3): 359-377. 
3. Bradley A. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognition 30(7): 1145-1159. 
4. Deng J. and Tsui H. (2002). A fast level set method for segmentation of low contrast noisy biomedical images, Pattern Recognition Letters 23(1-3): 161-169. Zbl0994.68514
5. Droske M., Meyer B., Rumpf M. and K. S. (2001). An adaptive level set method for medical image segmentation, Lecture Notes in Computer Science 2082: 416-422. Zbl0982.68546
6. Duda R., Hart P. and Stork D. (2000). Pattern Classification, 2nd edn, Wiley Interscience Publishers, New York. Zbl0968.68140
7. Friess T., Cristianini N. and Campbell C. (1998). The kernel adatron algorithm: A fast and simple learning procedure for support vector machines, Proceedings the 15th International Conference on Machine Learning, Morgan Kaufman Publishers, San Francisco, USA, pp. 188-196. 
8. Jeleń Ł., Krzyżak A. and Fevens T. (2006). Automated feature extraction for breast cancer grading with BloomRichardson scheme, International Journal of Computer Assisted Radiology and Surgery 1(1): 468-469. 
9. Kohonen T. (1990). The self-organizing map, Proceedings of Institute of Electrical and Electronics Engineers, 78(9): 1464-1480. 
10. Lee K. and Street W. (2000). Generalized Hough transforms with flexible templates, Proceedings of International Conference on Artificial Inteligence, Las Vegas, NV, pp. 1133-1139. 
11. Li C., Xu C., Gui C. and Fox M. (2005). Level set evolution without re-initialization: A new variational formulation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2005, San Diego, USA, pp. 430-436. 
12. Li S., Fevens T., Krzyżak A., Jin C. and Li S. (2006). Fast and robust clinical triple-region image segmentation using one level set function, Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention, Copenhagen, Denmark, 2: pp. 766-773. 
13. Nezafat R., Tabesh A., Akhavan S., Lucas C. and Zia M. (1998). Feature selection and classification for diagnosing breast cancer, Proceedings of International Association of Science and Technology for Development International Conference, Cancun, Mexico, pp. 310-313. 
14. Oja E. (1982). A siplified neuron modeled as a principal component analyzer, Journal of Mathematical Biology 15(3): 267-273. Zbl0488.92012
15. Osher S. and Sethian J. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics 79: 12-49. Zbl0659.65132
16. Ridler T. and Calvard S. (1978). Picture thresholding using an iterative selection, IEEE Transactions on System, Man and Cybernetics 8: 630-632. 
17. Schnorrenberg F., Pattichis C., Kyriacou K. and Schizas C. (1994). Detection of cell nuclei in breast cancer biopsies using receptive fields, IEEE Proceedings of Engineering in Medicine and Biology Society, pp. 649-650. 
18. Sethian J. and Adalsteinsson D. (1997). An overview of level set methods for etching, deposition and lithography development, IEEE Transactions on Semiconductor Manufacturing 10(1): 167-184. Zbl0952.65113
19. Street N. (2000). Xcyt: A system for remote cytological diagnosis and prognosis of breast cancer, in L. Jain (Ed.), Soft Computing Techniques in Breast Cancer Prognosis and Diagnosis, World Scientific Publishing, Singapore, pp. 297-322. 
20. Street W. N., Wolberg W. H. and Mangasarian O. L. (1993). Nuclear feature extraction for breast tumor diagnosis, Proceedings of the International Symposium on Electronic Imaging: Science and Technology, Vol. 1905, San Jose, CA, USA, pp. 861-870. 
21. Tsai A., Yezzi A., Wells III W., Tempany C., Tucker D., Fan A., Grimson W. and Willsky A. (2003). A shape-based approach to the segmentation of medical imagery using level sets, Medical Imaging 22(2): 137-154. 
22. Walker H. J. and Albertelli L. (1998). Breast cancer screening using evolved neural networks, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, San Diego, USA, pp. 1619-1624. 
23. Walker H. J., Albertelli L., Titkov Y., Kaltsatis P. and Seburyano G. (1998). Evolution of neural networks for the detection of breast cancer, Proceedings of International Joint Symposia on Inteligence and Systems pp. 34-40. 
24. Wolberg W. H., Street W. N. and Mangasarian O. L. (1994). Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates, Cancer Letters 77: 163-171. 
25. Wolberg W. and Mangasarian O. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology, Proceedings of the National Academy of Science, USA 87(23): 9193-9196. Zbl0709.92537
26. Zunic J., and Rosin P. (2002). A convexity measurement for polygons., Proceedings of the British Machine Vision Conference Cardiff, UK, 24: 173-182. Zbl1039.68588

1. Jan Rybka, Artur Janicki, Comparison of speaker dependent and speaker independent emotion recognition
2. Przemysław Mazurek, Dorota Oszutowska-Mazurek, From the Slit-Island Method to the Ising model: Analysis of irregular grayscale objects