Exploring the similarity of medical imaging classification problems

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

2 Citaten (Scopus)
2 Downloads (Pure)

Samenvatting

Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem of meta-learning – predicting which methods will perform well in an unseen classification problem, given previous experience with other classification problems. We investigate the first step of such an approach: how to quantify the similarity of different classification problems. We characterize datasets sampled from six classification problems by performance ranks of simple classifiers, and define the similarity by the inverse of Euclidean distance in this meta-feature space. We visualize the similarities in a 2D space, where meaningful clusters start to emerge, and show that the proposed representation can be used to classify datasets according to their origin with 89.3% accuracy. These findings, together with the observations of recent trends in machine learning, suggest that meta-learning could be a valuable tool for the medical imaging community.

Originele taal-2Engels
TitelIntravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis
Subtitel 6th Joint International Workshops, CVII-STENT 2017 and 2nd International Workshop, LABELS 2017 Held in Conjunction with MICCAI 2017, Proceedings
RedacteurenM.J. Cardoso, T. Arbel, et. al.
Plaats van productieCham
UitgeverijSpringer
Pagina's59-66
Aantal pagina's8
ISBN van elektronische versie978-3-319-67534-3
ISBN van geprinte versie9783319675336
DOI's
StatusGepubliceerd - 2017
Evenement2nd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, (LABELS 2017), 10-14 September 2017, Quebec City, Canada - Quebec City, Canada
Duur: 10 sep 201714 sep 2017

Publicatie series

NaamLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10552 LNCS
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349

Congres

Congres2nd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, (LABELS 2017), 10-14 September 2017, Quebec City, Canada
LandCanada
StadQuebec City
Periode10/09/1714/09/17

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  • Citeer dit

    Cheplygina, V., Moeskops, P., Veta, M., Dashtbozorg, B., & Pluim, J. P. W. (2017). Exploring the similarity of medical imaging classification problems. In M. J. Cardoso, T. Arbel, & et. al. (editors), Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis: 6th Joint International Workshops, CVII-STENT 2017 and 2nd International Workshop, LABELS 2017 Held in Conjunction with MICCAI 2017, Proceedings (blz. 59-66). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10552 LNCS). Springer. https://doi.org/10.1007/978-3-319-67534-3_7