title: Semi-supervised and active learning for automatic segmentation of Crohn's disease.
abstract: 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.
authors.affiliation: Department of Computer Science, ETH Zurich, Switzerland. dwarikanath.mahapatra@inf.ethz.ch
authors.forename: Dwarikanath
authors.initials: D
authors.lastname: Mahapatra
authors.affiliation: Department of Computer Science, ETH Zurich, Switzerland.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Schüffler
authors.affiliation: Department of Radiology, Academic Medical Center, The Netherlands.
authors.forename: Jeroen A W
authors.initials: JA
authors.lastname: Tielbeek
authors.affiliation: Department of Radiology, Academic Medical Center, The Netherlands.
authors.forename: Franciscus M
authors.initials: FM
authors.lastname: Vos
authors.affiliation: Department of Computer Science, ETH Zurich, Switzerland.
authors.forename: Joachim M
authors.initials: JM
authors.lastname: Buhmann
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003424:Crohn Disease / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity