Fusing Heterogeneous Features From Stacked Sparse Autoencoder for Histopathological Image Analysis

IEEE Journal of Biomedical and Health Informatics

Volumn 20, Issue 5, 2016, Pages 1377-1383

  Zhang, Xiaofan ^a   Dou, Hang ^b   Ju, Tao ^b   Xu, Jun ^c   Zhang, Shaoting ^a  

^a University of North Carolina at Charlotte   (United States)

^b WASHINGTON UNIVERSITY IN ST LOUIS   (United States)

^c NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

Author keywords

Breast lesion; feature fusion; histopathological image analysis; large scale image retrieval; stacked sparse autoencoder (SSAE)

Indexed keywords

COMPUTER AIDED DIAGNOSIS; CONTENT BASED RETRIEVAL; GRAPHIC METHODS; LEARNING SYSTEMS; MEDICAL IMAGING;

AUTO ENCODERS; BREAST LESION; CLASSIFICATION ACCURACY; CONTENT BASED IMAGE RETRIEVAL; FEATURE FUSION; HETEROGENEOUS FEATURES; HISTOPATHOLOGICAL IMAGE ANALYSIS; HISTOPATHOLOGICAL IMAGES;

IMAGE ANALYSIS;

HETEROGENEOUS NUCLEAR RNA;

ACCURACY; ALGORITHM; ARTICLE; BREAST; BREAST LESION; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; EXTRACELLULAR MATRIX; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; LEARNING; MICROSCOPY; PERFORMANCE; PROBABILITY; QUANTITATIVE ANALYSIS; SCANTY HAIR; TRAINING; VALIDATION PROCESS; BREAST TUMOR; CELL NUCLEUS; COMPUTER ASSISTED DIAGNOSIS; CYTOCHEMISTRY; DIAGNOSTIC IMAGING; FEMALE; MACHINE LEARNING; PATHOLOGY; PROCEDURES;

ALGORITHMS; BREAST; BREAST NEOPLASMS; CELL NUCLEUS; FEMALE; HISTOCYTOCHEMISTRY; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MACHINE LEARNING;

EID: 84987858748     PISSN: 21682194     EISSN: 21682208     Source Type: Journal    
DOI: 10.1109/JBHI.2015.2461671     Document Type: Article

References (43)

1. M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhushi, N. M. Rajpoot, and B. Yener, "Histopathological image analysis: A review, " IEEE Trans. Biomed. Eng., vol. 2, pp. 147-171, Dec. 2009.
2. A. N. Basavanhally, S. Ganesan, S. Agner, J. P. Monaco, M. D. Feldman, J. E. Tomaszewski, G. Bhanot, and A. Madabhushi, "Computerized image-based detection and grading of lymphocytic infiltration in her2+ breast cancer histopathology, " IEEE Trans. Biomed. Eng., vol. 57, no. 3, pp. 642-653, Mar. 2010.
3. B. Weyn, G. van de Wouwer, A. van Daele, P. Scheunders, D. van Dyck, E. van Marck, and W. Jacob, "Automated breast tumor diagnosis and grading based on wavelet chromatin texture description, " Cytometry, vol. 33, no. 1, pp. 32-40, 1998.
4. X. Zhang, W. Liu, M. Dundar, S. Badve, and S. Zhang, "Towards largescale histopathological image analysis: Hashing-based image retrieval, " IEEE Trans. Med. Imag., vol. 34, no. 2, pp. 496-506, Feb. 2015.
5. A. Tabesh, M. Teverovskiy, H.-Y. Pang, V. P. Kumar, D. Verbel, A. Kotsianti, and O. Saidi, "Multifeature prostate cancer diagnosis and Gleason grading of histological images, " IEEE Trans. Med. Imag., vol. 26, no. 10, pp. 1366-1378, Oct. 2007.
6. O. Sertel, J. Kong, U. V. Catalyurek, G. Lozanski, J. H. Saltz, and M. N. Gurcan, "Histopathological image analysis using model-based intermediate representations and color texture: Follicular lymphoma grading, " J. Signal Process. Syst., vol. 55, no. 1-3, pp. 169-183, 2009.
7. S. Doyle, S. Agner, A. Madabhushi, M. Feldman, and J. Tomaszewski, "Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features, " in Proc. Int. Symp. Biomed. Imag., 2008, pp. 496-499.
8. H. Müller, N. Michoux, D. Bandon, and A. Geissbuhler, "A review of content-based image retrieval systems in medical applications clinical benefits and future directions, " Int. J. Med. Informat., vol. 73, no. 1, pp. 1-23, 2004.
9. H. Müller, A. Geissbühler, and P. Ruch, "ImageCLEF 2004: Combining image and multi-lingual search for medical image retrieval, " in Multilingual Information Access for Text, Speech and Images. New York, NY, USA: Springer, 2005, pp. 718-727.
10. D. J. Foran, L. Yang et al., "Imageminer: A software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology, " J. Amer. Med. Informat. Assoc., vol. 18, no. 4, pp. 403-415, 2011.
11. A. Kumar, J. Kim, W. Cai, M. Fulham, and D. Feng, "Content-based medical image retrieval: A survey of applications to multidimensional and multimodality data, " J. Dig. Imag., vol. 26, no. 6, pp. 1025-1039, 2013.
12. G. Langs, H. Müller, B. H. Menze, and A. Hanbury, "VISCERAL: Towards large data in medical imaging-Challenges and directions, " in MCBR-CDS MICCAI Workshop (Lecture Notes in Computer Science). New York, NY, USA: Springer, vol. 7723, 2013.
13. J. C. Caicedo, A. Cruz, and F. A. Gonzalez, "Histopathology image classification using bag of features and kernel functions, " in Artificial Intelligence in Medicine. New York, NY, USA: Springer, 2009, pp. 126-135.
14. M. M. Dundar, S. Badve, G. Bilgin, V. Raykar, R. Jain, O. Sertel, and M. N. Gurcan, "Computerized classification of intraductal breast lesions using histopathological images, " IEEE Trans. Biomed. Eng., vol. 58, no. 7, pp. 1977-1984, Jul. 2011.
15. J. Sivic and A. Zisserman, "Video google: A text retrieval approach to object matching in videos, " in Proc. Int. Conf. Comput. Vis., 2003, pp. 1470-1477.
16. W. Liu, J. Wang, R. Ji, Y.-G. Jiang, and S.-F. Chang, "Supervised hashing with kernels, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2012, pp. 2074-2081.
17. X. Zhang, L. Yang, W. Liu, H. Su, and S. Zhang, "Mining histopathological images via composite hashing and online learning, " in Proc. Medical Image Computing and Computer-Assisted Intervention. New York, NY, USA: Springer, 2014, pp. 479-486.
18. S. Zhang, J. Huang, H. Li, and D. N. Metaxas, "Automatic image annotation and retrieval using group sparsity, " IEEE Trans. Syst. Man, Cybern. B, Cybern., vol. 42, no. 3, pp. 838-849, Jun. 2012.
19. P. Gehler and S. Nowozin, "On feature combination for multiclass object classification, " in Proc. Int. Conf. Comput. Vis., 2009, pp. 221-228.
20. R. Fagin, R. Kumar, and D. Sivakumar, "Efficient similarity search and classification via rank aggregation, " in Proc. ACM SIGMOD, 2003, pp. 301-312.
21. S. Zhang, M. Yang, T. Cour, K. Yu, and D. Metaxas, "Query specific rank fusion for image retrieval, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 4, pp. 803-815, Apr. 2015.
22. X. Zhang, H. Dou, T. Ju, and S. Zhang, "Fusing heterogeneous features for the image-guided diagnosis of intraductal breast lesions, " in Proc. Int. Symp. Biomed. Imag., 2015, pp. 1288-1291.
23. S. Petushi, F. U. Garcia, M. M. Haber, C. Katsinis, and A. Tozeren, "Largescale computations on histology images reveal grade-differentiating parameters for breast cancer, " BMC Med. Imag., vol. 6, no. 1, pp. 1-11, 2006.
24. C. Bilgin, C. Demir, C. Nagi, and B. Yener, "Cell-graph mining for breast tissue modeling and classification, " in Proc. IEEE Eng. Med. Biol. Soc., 2007, pp. 5311-5314.
25. C. Demir, S. H. Gultekin, and B. Yener, "Augmented cell-graphs for automated cancer diagnosis, " Bioinformatics, vol. 21, pp. ii7-ii12, 2005.
26. L. Yang, W. Chen, P. Meer, G. Salaru, M. D. Feldman, and D. J. Foran, "High throughput analysis of breast cancer specimens on the grid, " in Proc. Med. Image Comput. Comput.-Assisted Intervention Conf., 2007, pp. 617-625.
27. J. Sivic and A. Zisserman, "Video google: A text retrieval approach to object matching in videos, " in Proc. Int. Conf. Comput. Vis., 2003, pp. 1470-1477.
28. Y. Rui, T. S. Huang, and S.-F. Chang, "Image retrieval: Current techniques, promising directions, and open issues, " J. Visual Commun. Image Represent., vol. 10, no. 1, pp. 39-62, 1999.
29. Y. Gao, M. Wang, Z.-J. Zha, J. Shen, X. Li, and X. Wu, "Visual-textual joint relevance learning for tag-based social image search, " IEEE Trans, Image Process., vol. 22, no. 1, pp. 363-376, Jan. 2013.
30. Y. Gao, M. Wang, D. Tao, R. Ji, and Q. Dai, "3-D object retrieval and recognition with hypergraph analysis, " IEEE Trans. Image Process., vol. 21, no. 9, pp. 4290-4303, Sep. 2012.
31. D. Comaniciu, P. Meer, and D. J. Foran, "Image-guided decision support system for pathology, " Mach. Vis. Appl., vol. 11, no. 4, pp. 213-224, 1999.
32. Y. Song, W. Cai, and D. Feng, "Hierarchical spatial matching for medical image retrieval, " in Proc. ACM Int. Workshop Med. Multimedia Anal. Retrieval, 2011, pp. 1-6.
33. F. Schnorrenberg, C. Pattichis, C. Schizas, and K. Kyriacou, "Contentbased retrieval of breast cancer biopsy slides, " Technol. Health Care, vol. 8, no. 5, pp. 291-297, 2000.
34. L. Zheng, A. W. Wetzel, J. Gilbertson, andM. J. Becich, "Design and analysis of a content-based pathology image retrieval system, " IEEE Trans. Inf. Technol. Biomed., vol. 7, no. 4, pp. 249-255, Dec. 2003.
35. H. C. Akakin and M. N. Gurcan, "Content-based microscopic image retrieval system for multi-image queries, " IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 4, pp. 758-769, Jul. 2012.
36. D. G. Lowe, "Distinctive image features from scale-invariant keypoints, " Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, Nov. 2004.
37. L. Page, S. Brin, R. Motwani, and T. Winograd, "The pagerank citation ranking: Bringing order to the web, " 1999.
38. J. Xu, L. Xiang, R. Hang, and J. Wu, "Stacked sparse autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology, " in Proc. Int. Symp. Biomed. Imag., 2014, pp. 999-1002.
39. A. Ng, "Sparse autoencoder, " CS294A Lecture notes, p. 72, 2011.
40. D. Qin, S. Gammeter, L. Bossard, T. Quack, and L. Van Gool, "Hello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors, " in Proc. Comput. Vision Pattern Recog., 2011, pp. 777-784.
41. M. Richardson and P. Domingos, "The intelligent surfer: Probabilistic combination of link and content information in pagerank, " in Proc. Neural Inf. Process. Syst., 2001, pp. 1441-1448.
42. A. Makadia, V. Pavlovic, and S. Kumar, "Baselines for image annotation, " Int. J. Comput. Vis., vol. 90, no. 1, pp. 88-105, 2010.
43. H. Jegou, C. Schmid, H. Harzallah, and J. Verbeek, "Accurate image search using the contextual dissimilarity measure, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 1, pp. 2-11, Jan. 2010.