Semi-Supervised and Active Learning for Automatic Segmentation of Crohn’s Disease
Our proposed method combines semi supervised learning (SSL) and active learning (AL) for automatic detection and segmentation of Crohn’s disease (CD) from abdominal magnetic resonance (MR) images. Random forest (RF) classifiers are used due to fast SSL classification and capacity to interpret learned knowledge. Query samples for AL are selected by a novel information density weighted approach using context information, semantic knowledge and labeling uncertainty. Experimental results show that our proposed method combines the advantages of SSL and AL, and with fewer samples achieves higher classification and segmentation accuracy over fully supervised methods.
KeywordsRandom Forest Semantic Information Label Sample Semantic Knowledge Unlabeled Sample
- 4.Criminisi, A., Shotton, J.: Decision Forests for Computer Vision and Medical Image Analysis. Springer (2013)Google Scholar
- 7.Lewis, D., Catlett, J.: Heterogenous uncertainty sampling for supervised learning. In: ICML, pp. 148–156 (1994)Google Scholar
- 8.Mahapatra, D., Schüffler, P.J., Tielbeek, J., Buhmann, J.M., Vos, F.M.: A supervised learning based approach to detect crohn’s disease in abdominal mr volumes. In: Proc. MICCAI-ABD, pp. 97–106 (2012)Google Scholar
- 10.Settles, B.: Active learning literature survey. Tech. Rep. 1648, University of Wisconsin-Madison (January 2010)Google Scholar
- 11.Vos, F.M., et al.: Computational modeling for assessment of IBD: to be or not to be? In: Proc. IEEE EMBC, pp. 3974–3977 (2012)Google Scholar