A machine learning approach to identify prostate cancer areas in complex histological images

Advances in Intelligent Systems and Computing

Volumn 283, Issue , 2014, Pages 295-306

  Salman, Sadri ^a   Ma, Zhaoxuan ^b   Mohanty, Sambit ^b   Bhele, Sanica ^b   Chu, Yung Tien ^a   Knudsen, Beatrice ^b   Gertych, Arkadiusz ^a,b  

^a Department of Biomedical Engineering ^*  (United States)

^b CEDARS SINAI MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DISEASES; GRADING; GRAPHIC METHODS; LEARNING SYSTEMS; UROLOGY;

CLASSIFICATION SCHEME; HIGH RESOLUTION IMAGE; HISTOGRAMS OF ORIENTED GRADIENTS; HISTOLOGICAL IMAGES; INTENSITY HISTOGRAMS; MACHINE LEARNING APPROACHES; MANUAL ANNOTATION; SUPERVISED CLASSIFICATION;

IMAGE CLASSIFICATION;

EID: 84927632284     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-06593-9_26     Document Type: Article

References (13)

1. Fine, S.W., Amin, M.B., Berney, D.M., et al.: A contemporary update on pathology reporting for prostate cancer: biopsy and radical prostatectomy specimens. Eur. Urol. 62(1), 20–39 (2012)
2. Brimo, F., Montironi, R., Egevad, L., et al.: Contemporary Grading for Prostate Cancer: Implications for Patient Care. Eur. Urol. 63(5), 892–901 (2013)
3. Dole, S., Feldman, M.D., Shih, N., Tomaszewski, J., Madabhushi, A.: Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer. BMC Bioinformatics, 13–282 (2012)
4. Gorelick, L., Veksler, O., Gaed, M., et al.: Prostate Histopathology: Learning Tissue Component Histograms for Cancer Detection and Classification. IEEE Transactions on Medical Imaging 32(10), 1804–1818 (2013)
5. Loeffler, M., Greulich, L., Scheibe, P., Kahl, P., Shaikhibrahim, Z., Braumann, U.D., Kuska, J.P., Wernert, N.: Classifying prostate cancer malignancy by quantitative histomorphometry. J. Urol. 187(5), 1867–1875 (2012)
6. Doyle, S., Hwang, M., Shah, K., Madabhushi, A., Feldman, M., Tomaszewski, J.: Automated grading of prostate cancer using architectural and textural image features. In: 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1284–1287 (2007)
7. Peng, Y., Jiang, Y., Eisengart, L., Healy, M.A., Straus, F.H., Yang, X.J.: Computer-aided identification of prostatic adenocarcinoma: Segmentation of glandular structures. J. Pathol. Inform. 2, 33 (2011)
8. Nguyen, K., Sarkar, A., Jain, A.K.: Structure & Context in Prostatic Gland Segmentation and Classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 115–123. Springer, Heidelberg (2012)
9. Tabesh, A., Teverovskiy, M., Pang, H.Y., Kumar, V.P., Verbel, D., Kotsianti, A., Saidi, O.: Multifeature prostate cancer diagnosis and Gleason grading of histological images. IEEE Trans. Med. Imaging 26(10), 1366–1378 (2007)
10. Goode, A., Gilbert, B., Harkes, J., Jukic, D., Satyanarayanan, M.: OpenSlide: A vendor-neutral software foundation for digital pathology. J. Pathol. Inform. 4, 27 (2013)
11. Ruifrok, A.C., Johnston, D.A.: Quantification of histochemical staining by color deconvolution. Analytical & Quantitative Cytology and Histology 23(4), 291–299 (2001)
12. Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: Int. Conference on Computer Vision & Pattern Recognition, vol. 2, pp. 886–893 (2005)
13. Rand, W.M.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66(336), 846–850 (1971)