Characterization of tissue histopathology via predictive sparse decomposition and spatial pyramid matching

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

Volumn 8150 LNCS, Issue PART 2, 2013, Pages 91-98

  Chang, Hang ^a   Nayak, Nandita ^a   Spellman, Paul T ^b   Parvin, Bahram ^a  

^a LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

^b OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL VARIATION; CLASSIFICATION TECHNIQUE; CLINICAL OUTCOME; IMAGE-BASED CLASSIFICATION; PREDICTIVE MODELS; SPARSE DECOMPOSITION; SPATIAL PYRAMID MATCHING; WHOLE SLIDE IMAGES;

HISTOLOGY; TUMORS;

IMAGE MATCHING;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BIOPSY; COMPUTER ASSISTED DIAGNOSIS; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; MICROSCOPY; NEOPLASM; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; TUMOR CELL CULTURE;

ALGORITHMS; BIOPSY; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MICROSCOPY; NEOPLASMS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; TUMOR CELLS, CULTURED;

EID: 84897571868     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40763-5_12     Document Type: Conference Paper

References (14)

1. Kavukcuoglu, K., Ranzato, M., LeCun, Y.: Fast inference in sparse coding algorithms with applications to object recognition. Technical Report CBLL-TR-2008-12-01, Computational and Biological Learning Lab, Courant Institute, NYU (2008) 91, 92, 94, 95, 96, 97
2. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 2169-2178 (2006) 91, 92, 93, 94, 95, 96, 97
3. Gurcan,M., Boucheron, L., Can, A., Madabhushi, A., Rajpoot, N., Bulent, Y.: Histopathological image analysis: a review. IEEE Reviews in Biomedical Engineering 2, 147-171 (2009) 92
4. Ghaznavi, F., Evans, A., Madabhushi, A., Feldman, M.: Digital imaging in pathology: whole-slide imaging and beyond. Annu. Rev. Pathology 8, 331-359 (2012) 92
5. Ali, S., Madabhushi, A.: An integrated region-, boundry-, and shape-based active contour for multiple object overlap resolution in histological imagery. IEEE Transactions on Medical Imaging 31(7), 1448-1460 (2012) 92
6. Chang, H., Han, J., Borowsky, A., Loss, L., Gray, J.W., Spellman, P.T., Parvin, B.: Invariant delineation of nuclear architecture in glioblastoma multiforme for clinical and molecular association. IEEE Transactions on Medical Imaging 32(4), 670-682 (2013) 92
7. Kothari, S., Phan, J., Osunkoya, A., Wang, M.: Biological interpretation of morphological patterns in histopathological whole slide images. In: ACM Conference on Bioinformatics, Computational Biology and Biomedicine (2012) 92
8. Nayak, N., Chang, H., Borowsky, A., Spellman, P.T., Parvin, B.: Classification of tumor histopathology via sparse feature learning. In: International Symposium on Biomedical Imaging (2013) 92
9. Le, Q., Han, J., Gray, J., Spellman, P., Borowsky, A., Parvin, B.: Learning invariant features from tumor signature. In: International Symposium on Biomedical Imaging, pp. 302-305 (2012) 92, 97
10. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge (VOC 2012) Results (2012), http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html 92
11. Vedaldi, A., Zisserman, A.: Efficient additive kernels via explicit feature maps. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(3), 480-492 (2012) 93
12. Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 1794-1801 (2009) 93, 94, 95, 96, 97
13. Fan, R.E., Chang, K.W., Hsieh, C.J.,Wang, X.R., Lin, C.J.: LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research 9, 1871-1874 (2008) 93
14. Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Supervised dictionary learning. In: NIPS (2008) 97