Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2012, Pages 964-971

  Xu, Yan ^a,b   Zhu, Jun Yan ^b,c   Chang, Eric ^b   Tu, Zhuowen ^b,d  

^a BEIHANG UNIVERSITY   (China)

^b MICROSOFT RESEARCH ASIA   (China)

^c TSINGHUA UNIVERSITY   (China)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ABNORMAL PATTERNS; CANCER CELLS; CANCER TISSUES; COLON CANCER; INTEGRATED FRAMEWORKS; LEARNING METHODS; MULTIPLE-INSTANCE LEARNING; SEGMENTATION AND CLUSTERING;

CELLS; COMPUTER VISION; IMAGE SEGMENTATION; LEARNING SYSTEMS; TISSUE;

DISEASES;

EID: 84866665353     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6247772     Document Type: Conference Paper

References (28)

1. D. Altunbay, C. Cigir, C. Sokmensuer, and C. Gunduz-Demir. Color graphs for automated cancer diagnosis and grading. IEEE Transaction on Biomedical Engineering, 57(3):665-674, 2010. 2
2. S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-instance learning. In NIPS, 2002. 1, 5
3. B. Babenko, P. Dollár, Z. Tu, and S. Belongie. Simultaneous learning and alignment: Multi-instance and multi-pose learning. In ECCV workshop on Faces in Real-Life Images, 2008. 1, 2, 3
4. J. Bi and J. Liang. Multiple instance learning of pulmonary embolism detection with geodesic distance along vascular structure. In CVPR, 2007. 2
5. L. E. Boucheron. Object- and Spatial-Level Quantitative Analysis of Multispectral Histopathology Images for Detection and Characterization of Cancer. PhD thesis, Mar 2008. 1, 2
6. P. Dollár, B. Babenko, S. Belongie, P. Perona, and Z. Tu. Multiple component learning for object detection. In ECCV, 2008. 1, 2
7. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification (2nd Edition). Wiley-Interscience, 2nd edition, Nov. 2001. 1, 5
8. M. Dundar, S. Badve, V. Raykar, R. Jain, O. Sertel, and M. Gurcan. A multiple instance learning approach toward optimal classification of pathology slides. In ICPR, 2010. 2
9. A. Esgiar, R. Naguib, B. Sharif, M. Bennett, and A. Murray. Fractal analysis in the detection of colonic cancer images. IEEE Transaction on Information Technology in Biomedicine, 6(1):54-58, 2002. 1, 5, 6
10. G. Fung, M. Dundar, B. Krishnapuram, and R. B. Rao. Multiple instance learning for computer aided diagnosis. In NIPS, 2006. 2
11. P.-W. Huang and C.-H. Lee. Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans. Medical Imaging, 28(7):1037-1050, 2009. 1, 2, 5
12. J. Kong, O. Sertel, H. Shimada, K. L. Boyer, J. H. Saltz, and M. N. Gurcan. Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation. Pattern Recogn., 42(6):1080-1092, 2009. 1, 2
13. D. G. Lowe. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision, 60:91-110, 2004. 5
14. L. Lu, J. Bi, M. Wolf, and M. Salganicoff. Effective 3d object detection and regression using probabilistic segmentation features in ct images. In CVPR, 2011. 2
15. A. Madabhushi. Digital pathology image analysis: opportunities and challenges. Imaging in Medicine, 1(1):7-10, 2009. 1
16. O. Maron and T. Lozano-Pérez. A framework for multiple-instance learning. In NIPS, 1997. 1
17. L. Mason, J. Baxter, P. Bartlett, and M. Frean. Boosting algorithms as gradient descent. In NIPS, 2000. 3, 5, 6
18. T. Ojala, M. Pietikinen, and T. Menp. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. PAMI, 24(7):971-987, 2002. 5
19. S. Park, D. Sargent, R. Lieberman, and U. Gustafsson. Domain-specific image analysis for cervical neoplasia detection based on conditional random fields. IEEE Trans. Medical Imaging, 30(3):867 -78, 2011. 1
20. A. Quattoni, S. Wang, L. Morency, M. Collins, and T. Darrell. Hidden conditional random fields. IEEE Trans. PAMI, 29(10):1848-1852, 2007. 2
21. A. Tabesh, M. Teverovskiy, H.-Y. Pang, V. Kumar, D. Verbel, A. Kotsianti, and O. Saidi. Multifeature prostate cancer diagnosis and gleason grading of histological images. IEEE Trans. Medical Imaging, 26(10):1366-78, 2007. 1, 2, 5, 6
22. Z. Tu and X. Bai. Auto-context and its application to high-level vision tasks and 3d brain image segmentation. IEEE Trans. PAMI, 21(10):1744-1757, 2010. 1
23. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman. Multiple kernels for object detection. In ICCV, 2009. 1, 5
24. P. A. Viola, J. Platt, and C. Zhang. Multiple instance boosting for object detection. In NIPS, 2005. 1, 2, 3, 5, 6
25. Z.-J. Zha, T. Mei, J. Wang, G.-J. Qi, and Z. Wang. Joint multi-label multiinstance learning for image classification. In CVPR, 2008. 2
26. D. Zhang, F.Wang, L. Si, and T. Li. M3ic: Maximum margin multiple instance clustering. In IJCAI, 2009. 1, 2
27. Z.-H. Zhou and M.-L. Zhang. Multi-instance multilabel learning with application to scene classification. In NIPS, 2007. 2
28. X. Zhu. Semi-supervised learning literature survey. Computer Science TR 1530, University of Wisconsin-Madison, 2008. 1