Abstract
In this chapter we describe an application for efficient error-free labeling of medical images. In this scenario, the compilation of a complete training set for building a realistic model of a given class of samples is not an easy task, making the process tedious and time consuming. For this reason, there is a need for interactive labeling applications that minimize the effort of the user while providing error-free labeling. We propose a new algorithm that is based on data similarity in feature space. This method actively explores data in order to find the best label-aligned clustering and exploits it to reduce the labeler effort, that is measured by the number of “clicks. Moreover, error-free labeling is guaranteed by the fact that all data and their labels proposals are visually revised by en expert.
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References
Bashar, M.K., et al.: Automatic detection of informative frames from wireless capsule endoscopy images. Medical Image Analysis 14(3), 449–470 (2010)
Bekel, H., Heidemann, G., Ritter, H.: Interactive image data labeling using self-organizing maps in an augmented reality scenario. Neural Networks 18(5-6), 566–574 (2005)
Biswas, A., Jacobs, D.: Active image clustering: Seeking constraints from humans to complement algorithms. In: CVPR (2012)
Coimbra, M.T., Cunha, J.P.S.: MPEG-7 visual descriptors: Contributions for automated feature extraction in capsule endoscopy. IEEE TCSVT 16(5), 628–637 (2006)
Dasgupta, S., Hsu, D.: Hierarchical sampling for active learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 208–215. ACM (2008)
Donmez, P., Carbonell, J.G., Bennett, P.N.: Dual Strategy Active Learning. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 116–127. Springer, Heidelberg (2007)
Drozdzal, M., Seguí, S., Malagelada, C., Azpiroz, F., Vitrià, J., Radeva, P.: Interactive Labeling of WCE Images. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 143–150. Springer, Heidelberg (2011)
Dwyer, K., Holte, R.: Decision Tree Instability and Active Learning. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 128–139. Springer, Heidelberg (2007)
Freund, Y., Sebastian Seung, H., Shamir, E., Tishby, N.: Selective sampling using the query by committee algorithm. Mach. Learn. 28, 133–168 (1997)
Hospedales, T.M., Gong, S., Xiang, T.: Finding rare classes: Active learning with generative and discriminative models. IEEE Transactions on Knowledge and Data Engineering 99(PrePrints) (2011)
Igual, L., et al.: Automatic discrimination of duodenum in wireless capsule video endoscopy. IFMBE Proceedings 22, 1536–1539 (2008)
Jung, Y.S., et al.: Active blood detection in a high resolution capsule endoscopy using color spectrum transformation. In: ICBEI, pp. 859–862 (2008)
Kang, J., Doraiswami, R.: Real-time image processing system for endoscopic applications. In: IEEE CCECE 2003, vol. 3, pp. 1469–1472 (2003)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience (2004)
Malagelada, C., De lorio, F., Seguí, S., Mendez, S., Drozdzal, M., Vitria, J., Radeva, P., Santos, J., Accarino, A., Malagelada, J.R., Azpiroz, F.: Functional gut disorders or disordered gut function? small bowel dysmotility evidenced by an original technique. Neurogastroenterology and Motility 24(3), 223-e105 (2012)
Malagelada, C., et al.: New insight into intestinal motor function via noninvasive endoluminal image analysis. Gastroenterology 135(4), 1155–1162 (2008)
McCallum, A., Nigam, K.: Employing em and pool-based active learning for text classification. In: Proceedings of the Fifteenth International Conference on Machine Learning, ICML 1998, pp. 350–358. Morgan Kaufmann Publishers Inc., San Francisco (1998)
Nguyen, H.T., Smeulders, A.: Active learning using pre-clustering. In: Proceedings of the Twenty-First International Conference on Machine Learning, ICML 2004, p. 79. ACM, New York (2004)
Settles, B.: Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison (2009)
Tian, Y.: A face annotation framework with partial clustering and interactive labeling. In: International Conf. on Computer Vision and Pattern Recognition, p. 7 (2007)
Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2, 45–66 (2002)
Vilarino, F., et al.: Intestinal motility assessment with video capsule endoscopy: Automatic annotation of phasic intestinal contractions. IEEE TMI 29(2), 246–259 (2010)
Ward Jr., J.H.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58(301), 236–244 (1963)
Xu, Z., Yu, G., Tresp, V., Xu, X., Wang, J.: Representative Sampling for Text Classification Using Support Vector Machines. In: Sebastiani, F. (ed.) ECIR 2003. LNCS, vol. 2633, pp. 393–407. Springer, Heidelberg (2003)
Zhang, L., Tong, Y., Ji, Q.: Active Image Labeling and Its Application to Facial Action Labeling. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 706–719. Springer, Heidelberg (2008)
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Drozdzal, M., Seguí, S., Radeva, P., Malagelada, C., Azpiroz, F., Vitrià, J. (2013). An Application for Efficient Error-Free Labeling of Medical Images. In: Multimodal Interaction in Image and Video Applications. Intelligent Systems Reference Library, vol 48. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35932-3_1
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DOI: https://doi.org/10.1007/978-3-642-35932-3_1
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