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, Conference Contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Series: Informatik aktuell
City/Town: Wiesbaden
Pages Range: 321-326
Conference Proceedings Title: Informatik aktuell
Event location: Heidelberg, DEU
ISBN: 9783658369316
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)
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.), Informatik aktuell (pp. 321-326). Heidelberg, DEU, DE: Wiesbaden: Springer Science and Business Media Deutschland GmbH.
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, DEU Ed. Klaus Maier-Hein, Thomas M. Deserno, Heinz Handels, Andreas Maier, Christoph Palm, Thomas Tolxdorff, Wiesbaden: Springer Science and Business Media Deutschland GmbH, 2022. 321-326.
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