Abstract
In this paper, we propose to use advanced classification techniques with shape features for nuclei classification in tissue microarray images of renal cell carcinoma. Our aim is to improve the classification accuracy in distinguishing between healthy and cancerous cells. The approach is inspired by natural language processing: several features are extracted from the automatically segmented nuclei and quantized to visual words, and their co-occurrences are encoded as visual topics. To this end, a generative model, the probabilistic Latent Semantic Analysis (pLSA) is learned from quantized shape descriptors (visual words). Finally, we extract from the learned models a generative score, that is used as input for new classifiers, defining a hybrid generative-discriminative classification algorithm. We compare our results with the same classifiers on the feature set to assess the increase of accuracy when we apply pLSA. We demonstrate that the feature space created using pLSA achieves better accuracies than the original feature space.
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Bicego, M., Castellani, U., Murino, V.: Using hidden markov models and wavelets for face recognition. In: ICIAP, pp. 52–56 (2003)
Bicego, M., Cristani, M., Murino, V., Pękalska, E.z., Duin, R.P.W.: Clustering-based construction of hidden markov models for generative kernels. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds.) EMMCVPR 2009. LNCS, vol. 5681, pp. 466–479. Springer, Heidelberg (2009)
Bicego, M., Lovato, P., Ferrarini, A., Delledonne, M.: Biclustering of expression microarray data with topic models. In: Proceedings of the 2010 20th International Conference on Pattern Recognition, ICPR 2010, Washington, DC, USA, pp. 2728–2731 (2010)
Bicego, M., Lovato, P., Oliboni, B., Perina, A.: Expression microarray classification using topic models. In: Proceedings of the 2010 ACM Symposium on Applied Computing, SAC 2010, New York, NY, USA, pp. 1516–1520 (2010)
Bicego, M., Murino, V.: Investigating hidden markov models’ capabilities in 2D shape classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 281–286 (2004)
Bicego, M., Murino, V., Figueiredo, M.A.: Similarity-based classification of sequences using hidden markov models. Pattern Recognition 37(12), 2281–2291 (2004)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Bosch, A., Zisserman, A., Muñoz, X.: Scene classification via plsa. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006)
Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: CIVR 2007: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 401–408. ACM, New York (2007)
Boykov, Y., Veksler, O., Zabih, R.: Efficient approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(12), 1222–1239 (2001)
Castellani, U., Perina, A., Murino, V., Bellani, M., Rambaldelli, G., Tansella, M., Brambilla, P.: Brain morphometry by probabilistic latent semantic analysis. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 177–184. Springer, Heidelberg (2010)
Cristani, M., Perina, A., Castellani, U., Murino, V.: Geo-located image analysis using latent representations. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008)
Duin, R.P.W.: Prtools, a matlab toolbox for pattern recognition version 4.0.14 (2005), http://www.prtools.org/ , http://www.prtools.org/
Elżbieta Pekalska, E., Duin, R.P.: The Dissimilarity Representation for Pattern Recognition. Foundations and Applications. World Scientific, Singapore (2005)
Fuchs, T.J., Wild, P.J., Moch, H., Buhmann, J.M.: Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 1–8. Springer, Heidelberg (2008)
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital image processing using matlab (2003), 993475
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 42(1-2), 177–196 (2001)
Jaakkola, T.S., Haussler, D.: Exploiting generative models in discriminative classifiers. In: Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems, NIPS 1998, Cambridge, MA, USA, pp. 487–493 (1999)
Kononen, J., Bubendorf, L., et al.: Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat. Med. 4(7), 844–847 (1998)
Lasserre, J.A., Bishop, C.M., Minka, T.P.: Principled hybrids of generative and discriminative models. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006, Washington, DC, USA, vol. 1, pp. 87–94 (2006)
Ng, A.Y., Jordan, M.I.: On discriminative vs generative classifiers: A comparison of logistic regression and naive Bayes. In: Advances in Neural Information Processing Systems, NIPS 2002, pp. 841–848 (2002)
Perina, A., Cristani, M., Castellani, U., Murino, V., Jojic, N.: A hybrid generative/discriminative classification framework based on free-energy terms. In: IEEE 12th International Conference on Computer Vision, ICCV 2009, October 2-29, pp. 2058–2065 (2009)
Perina, A., Cristani, M., Castellani, U., Murino, V., Jojic, N.: A hybrid generative/discriminative classification framework based on free-energy terms. In: Proceedings of the IEEE International Conference on Computer Vision, ICCV 2009, pp. 2058–2065 (2009)
Rubinstein, Y.D., Hastie, T.: Discriminative vs informative learning. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 49–53. AAAI Press, Menlo Park (1997)
Russell, B.C., Freeman, W.T., Efros, A.A., Sivic, J., Zisserman, A.: Using multiple segmentations to discover objects and their extent in image collections. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 1605–1614 (2006)
Schüffler, P.J., Fuchs, T.J., Ong, C.S., Roth, V., Buhmann, J.M.: Computational TMA analysis and cell nucleus classification of renal cell carcinoma. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds.) Pattern Recognition. LNCS, vol. 6376, pp. 202–211. Springer, Heidelberg (2010)
Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering objects and their localization in images. In: Proceedings of the IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 370–377 (2005)
Tsuda, K., Kawanabe, M., Rätsch, G., Sonnenburg, S., Müller, K.R.: A new discriminative kernel from probabilistic models. Neural Computation 14, 2397–2414 (2002)
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Ulaş, A., Schüffler, P.J., Bicego, M., Castellani, U., Murino, V. (2011). Hybrid Generative-Discriminative Nucleus Classification of Renal Cell Carcinoma. In: Pelillo, M., Hancock, E.R. (eds) Similarity-Based Pattern Recognition. SIMBAD 2011. Lecture Notes in Computer Science, vol 7005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24471-1_6
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DOI: https://doi.org/10.1007/978-3-642-24471-1_6
Publisher Name: Springer, Berlin, Heidelberg
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