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TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification

Niemeijer, Joshua und Ehrhardt, Jan und Uzunova, Hristina und Handels, Heinz (2024) TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification. In: 9th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2024, held in conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, 15187, Seiten 69-78. 9th International Workshop, SASHIMI 2024, Held in Conjunction with MICCAI 2024, 2024-10-10, Marrakesh, Morocco. doi: 10.1007/978-3-031-73281-2_7. ISBN 978-303173280-5. ISSN 0302-9743.

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Kurzfassung

The usage of medical image data for the training of large-scale machine learning approaches is particularly challenging due to its scarce availability and the costly generation of data annotations, typically requiring the engagement of medical professionals. The rapid development of generative models enables us to tackle this problem by generating large amounts of realistic synthetic data for the training process. However, randomly choosing synthetic samples, might not be an optimal strategy. In this work, we investigate the targeted generation of synthetic training data, in order to improve the accuracy and robustness of image classification. Therefore, our approach aims to guide the generative model to synthesize data with high epistemic uncertainty, since large measures of epistemic uncertainty indicate underrepresented data points in the training set. During the image generation we feed images reconstructed by an auto encoder into the classifier and compute the mutual information over the class-probability distribution as a measure for uncertainty. We alter the feature space of the autoencoder through an optimization process with the objective of maximizing the classifier uncertainty on the decoded image. By training on such data we improve the performance and robustness against test time data augmentations and adversarial attacks on several classifications tasks.

elib-URL des Eintrags:https://elib.dlr.de/207829/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Niemeijer, JoshuaJoshua.Niemeijer (at) dlr.dehttps://orcid.org/0000-0002-2417-8749NICHT SPEZIFIZIERT
Ehrhardt, Janjan.ehrhardt (at) uni-luebeck.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Uzunova, Hristinahristina.uzunova (at) dfki.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Handels, Heinzheinz.handels (at) uni-luebeck.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:10 Oktober 2024
Erschienen in:9th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2024, held in conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Nein
Band:15187
DOI:10.1007/978-3-031-73281-2_7
Seitenbereich:Seiten 69-78
Name der Reihe:Lecture Notes in Computer Science
ISSN:0302-9743
ISBN:978-303173280-5
Status:veröffentlicht
Stichwörter:synthetic data generation, generalization, robustness, machine learning
Veranstaltungstitel:9th International Workshop, SASHIMI 2024, Held in Conjunction with MICCAI 2024
Veranstaltungsort:Marrakesh, Morocco
Veranstaltungsart:internationale Konferenz
Veranstaltungsdatum:10 Oktober 2024
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Verkehr
HGF - Programmthema:Straßenverkehr
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V ST Straßenverkehr
DLR - Teilgebiet (Projekt, Vorhaben):V - KoKoVI - Koordinierter kooperativer Verkehr mit verteilter, lernender Intelligenz, R - SynthBAD
Standort: Braunschweig
Institute & Einrichtungen:Institut für Verkehrssystemtechnik > Kooperative Systeme, BS
Hinterlegt von: Niemeijer, Joshua
Hinterlegt am:28 Okt 2024 12:59
Letzte Änderung:13 Nov 2024 11:59

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