Semi-supervised and active learning for automatic segmentation of Crohn's disease

Dwarikanath Mahapatra, Peter J. Schüffler, Jeroen A.W. Tielbeek, Franciscus M. Vos, Joachim M. Buhmann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

27 Zitate (Scopus)

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.

OriginalspracheEnglisch
TitelMedical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Seiten214-221
Seitenumfang8
AuflagePART 2
DOIs
PublikationsstatusVeröffentlicht - 2013
Extern publiziertJa
Veranstaltung16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Dauer: 22 Sept. 201326 Sept. 2013

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NummerPART 2
Band8150 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Land/GebietJapan
OrtNagoya
Zeitraum22/09/1326/09/13

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