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Generative-Adversarial-Network-Based Data Augmentation for the Classification of Craniosynostosis

Kaiser, Christian 1; Schaufelberger, Matthias 1; Kühle, Reinald Peter; Wachter, Andreas ORCID iD icon 1; Weichel, Frederic; Hagen, Niclas; Ringwald, Friedemann; Eisenmann, Urs; Engel, Michael; Freudlsperger, Christian; Nahm, Werner 1
1 Institut für Biomedizinische Technik (IBT), Karlsruher Institut für Technologie (KIT)

Abstract:

Craniosynostosis is a congenital disease characterized by the premature closure of one or multiple sutures of the infant’s skull. For diagnosis, 3D photogrammetric scans are a radiation-free alternative to computed tomography. However, data is only sparsely available and the role of data augmentation for the classification of craniosynostosis has not yet been analyzed. In this work, we use a 2D distance map representation of the infants’ heads with a convolutional-neural-network-based classifier and employ a generative adversarial network (GAN) for data augmentation. We simulate two data scarcity scenarios with 15% and 10% training data and test the influence of different degrees of added synthetic data and balancing underrepresented classes. We used total accuracy and F1-score as a metric to evaluate the final classifiers. For 15% training data, the GAN-augmented dataset showed an increased F1-score up to 0.1 and classification accuracy up to 3 %. For 10% training data, both metrics decreased. We present a deep convolutional GAN capable of creating synthetic data for the classification of craniosynostosis. Using a moderate amount of synthetic data using a GAN showed slightly better performance, but had little effect overall. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000151296
Veröffentlicht am 12.10.2022
Originalveröffentlichung
DOI: 10.1515/cdbme-2022-1005
Scopus
Zitationen: 1
Dimensions
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Biomedizinische Technik (IBT)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 02.09.2022
Sprache Englisch
Identifikator ISSN: 2364-5504
KITopen-ID: 1000151296
Erschienen in Current Directions in Biomedical Engineering
Verlag De Gruyter
Band 8
Heft 2
Seiten 17–20
Schlagwörter Generative adversarial network; classification; craniosynostosis; data augmentation
Nachgewiesen in Dimensions
Scopus
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