GAN-based Augmentation of Mammograms to Improve Breast Lesion Detection

El-Ghoussani A, Rodriguez Salas D, Seuret M, Maier A (2022)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2022

Publisher: Springer Vieweg Wiesbaden

Series: Informatik aktuell

City/Town: Wiesbaden

Pages Range: 321--326

Conference Proceedings Title: Bildverarbeitung für die Medizin 2022

Event location: Heidelberg DE

ISBN: 978-3-658-36932-3

DOI: 10.1007/978-3-658-36932-3_66

Abstract

Mammography is an important part of breast cancer diagnostics, as it allows to inspect the inner breast structure without physically penetrating breast tissue. Commonly, mammograms tend to vary in their visual appearance based on the specific device and the circumstances under which the mammogram is acquired. Such images could cause artificial intelligence algorithms to fail as they can introduce an undesired variation into the data. This study intends to put these images to use by utilizing a cycle-consistent Generative Adversarial Network (GAN) in order to augment the training data by diversifying instances of each visual domain into all the available ones. A publicly available dataset was augmented to train a detection network; the GANs used for the augmentation were trained with an in-house dataset with three visually different domains.Results show that using our augmentation technique consistently increases the detection performance by reaching a mean average precision of up to 0.82 against 0.77 without augmenting the data.

Authors with CRIS profile

Dalia Rodriguez Salas Lehrstuhl für Informatik 5 (Mustererkennung) Mathias Seuret Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

How to cite

APA:

El-Ghoussani, A., Rodriguez Salas, D., Seuret, M., & Maier, A. (2022). GAN-based Augmentation of Mammograms to Improve Breast Lesion Detection. In Klaus Maier-Hein, Thomas M. Deserno, Heinz Handels, Andreas Maier, Christoph Palm, Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2022 (pp. 321--326). Heidelberg, DE: Wiesbaden: Springer Vieweg Wiesbaden.

MLA:

El-Ghoussani, Amir, et al. "GAN-based Augmentation of Mammograms to Improve Breast Lesion Detection." Proceedings of the Bildverarbeitung für die Medizin 2022, Heidelberg Ed. Klaus Maier-Hein, Thomas M. Deserno, Heinz Handels, Andreas Maier, Christoph Palm, Thomas Tolxdorff, Wiesbaden: Springer Vieweg Wiesbaden, 2022. 321--326.

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