Automated nuclear pleomorphism scoring in breast cancer histopathology images using deep neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9468, Issue , 2015, Pages 269-276

  Maqlin, P ^a   Thamburaj, Robinson ^a   Mammen, Joy John ^b   Manipadam, Marie Theresa ^b  

^a MADRAS CHRISTIAN COLLEGE   (India)

^b CHRISTIAN MEDICAL COLLEGE   (India)

Author keywords

Breast cancer; Deep neural networks; Nuclear pleomorphism

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPLEX NETWORKS; DISEASES; GRADING; HISTOLOGY;

BREAST CANCER; BREAST CANCER GRADING; CLASSIFICATION ACCURACY; DEEP NEURAL NETWORKS; MEAN AND STANDARD DEVIATIONS; NUCLEAR PLEOMORPHISM; OBSERVER VARIABILITY; TRAINING AND TESTING;

MEDICAL IMAGING;

EID: 84955260373     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-26832-3_26     Document Type: Conference Paper

References (12)

1. Cosatto, E., Miller, M., Graf, H.P., Meyer, J.S.: Grading nuclear pleomorphism on histological micrographs. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4 December 2008
2. Dunne, B., Going, J.J.: Scoring nuclear pleomorphism in breast cancer. Histopathology 39(3), 259–265 (2001)
3. Elston, C.W., Ellis, I.O.: Pathological prognostic factors in breast cancer. i. the value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 41(3A), 151–152 (2002). discussion 152–3
4. Frierson, H.F. Jr., Wolber, R.A., Berean, K.W., Franquemont, D.W., Gaffey, M.J., Boyd, J.C., Wilbur, D.C.: Interobserver reproducibility of the nottingham modification of the bloom and richardson histologic grading scheme for infiltrating ductal carcinoma. Am. J. Clin. Pathol. 103(2), 195–198 (1995)
5. Irshad, H., Veillard, A., Roux, L., Racoceanu, D.: Methods for nuclei detection, segmentation, and classification in digital histopathology: A review of - current status and future potential. Biomed. Eng. IEEE Rev. 7, 97–114 (2014)
6. Paramanandam, M., Thamburaj, R., Manipadam, M.T., Nagar, A.K.: Boundary extraction for imperfectly segmented nuclei in breast histopathology images – a convex edge grouping approach. In: Barneva, R.P., Brimkov, V.E., Šlapal, J. (eds.) IWCIA 2014. LNCS, vol. 8466, pp. 250–261. Springer, Heidelberg (2014)
7. Dalle, J.R., Li, H., Huang, C.H., Leow, W.K., Racoceanu, D., Putti, T.C.: Nuclear pleomorphism scoring by selective cell nuclei detection
8. Tanaka, M., Okutomi, M.: A novel inference of a restricted boltzmann machine. In: Proceedings of the 2014 22Nd International Conference on Pattern Recognition, ICPR 2014, pp. 1526–1531. IEEE Computer Society, Washington, DC, USA (2014)
9. Tavassoli, F.A., Devilee, P.: International Agency for Research on Cancer, and World Health Organization. Pathology and Genetics of Tumours of the Breast and Female Genital Organs. Who/IARC Classification of Tumours. IARC Press, Lyon (2003)
10. Wang, H., Cruz-Roa, A., Basavanhally, A., Gilmore, H., Shih, N., Feldman, M., Tomaszewski, J., Gonzalez, F., Madabhushi, A.: Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J. Med. Imaging 1(3), 034003 (2014)
11. Watson, S.K., Watson, G.H.: Assessment of nuclear pleomorphism by image analysis, WO Patent App. PCT/GB2004/000, 400, August 26 2004
12. William, H., Nick, W., Olvi, L., Mangasarian, L.: Importance of nuclear morphology in breast cancer prognosis. Clin. Cancer Res. 5(11), 3542–3548 (1999)