Large margin aggregation of local estimates for medical image classification

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

Volumn 8674 LNCS, Issue PART 2, 2014, Pages 196-203

  Song, Yang ^a   Cai, Weidong ^a   Huang, Heng ^b   Zhou, Yun ^c   Feng, David Dagan ^a   Chen, Mei ^d  

^a UNIVERSITY OF SYDNEY   (Australia)

^b University of Texas at Arlington   (United States)

^c JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTERIZED TOMOGRAPHY; MEDICAL COMPUTING; MEDICAL IMAGING; PULMONARY DISEASES;

CLASSIFICATION MODELS; FEATURE SPACE; HIGH-RESOLUTION COMPUTED TOMOGRAPHY; IMAGE PATCHES; INTERSTITIAL LUNG DISEASE; INTRA-CLASS VARIATION; LARGE MARGINS; REFERENCE IMAGE; STATE OF THE ART;

IMAGE CLASSIFICATION;

EID: 84906980116     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10470-6_25     Document Type: Conference Paper

References (12)

1. Depeursinge, A., Vargas, A., Platon, A., Geissbuhler, A., Poletti, P.A., Muller, H.: Building a reference multimedia database for interstitial lung diseases. Comput. Med. Imaging Graph. 36(3), 227-238 (2012)
2. Dong, J., Xia, W., Chen, Q., Feng, J., Huang, Z., Yan, S.: Subcategory-aware object classification. In: CVPR, pp. 827-834 (2013)
3. Hoai, M., Zisserman, A.: Discriminative sub-categorization. In: CVPR, pp. 1666-1673 (2013)
4. Liao, S., Gao, Y., Shen, D.: Sparse patch based prostate segmentation in CT images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 385-392. Springer, Heidelberg (2012)
5. Liu, J., Ji, S., Ye, J.: SLEP: sparse learning with efficient projections. Arizona State University (2009), http://www.public.asu.edu/~jye02/ Software/SLEP/
6. Liu, M., Lu, L., Ye, X., Yu, S., Salganicoff, M.: Sparse classification for computer aided diagnosis using learned dictionaries. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 41-48. Springer, Heidelberg (2011)
7. Song, Y., Cai, W., Huang, H., Wang, X., Zhou, Y., Fulham, M., Feng, D.: Lesion detection and characterization with context driven approximation in thoracic FDG PET-CT images of NSCLC studies. IEEE Trans. Med. Imag. 33(2), 408-421 (2014)
8. Song, Y., Cai, W., Zhou, Y., Feng, D.: Feature-based image patch approximation for lung tissue classification. IEEE Trans. Med. Imag. 32(4), 797-808 (2013)
9. Wang, H., Nie, F., Huang, H., Risacher, S., Ding, C., Saykin, A.J., Shen, L.: ADNI: Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance. In: ICCV, pp. 557-562 (2011)
10. Weinberger, K., Saul, L.: Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research 10, 207-244 (2009)
11. Xu, Y., Gao, X., Lin, S., Wong, D.W.K., Liu, J., Xu, D., Cheng, C., Cheung, C.Y., Wong, T.Y.: Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 468-475. Springer, Heidelberg (2013)
12. Zhu, M., Martinez, A.: Subclass discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell. 28(8), 1274-1286 (2006)