Deep learning-based denoising of mammographic images using physics-driven data augmentation

Eckert D, Vesal S, Ritschl L, Kappler S, Maier A (2020)

Publication Type: Conference contribution

Publication year: 2020

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: Springer

Pages Range: 94-100

Conference Proceedings Title: Informatik aktuell

Event location: Berlin DE

ISBN: 9783658292669

DOI: 10.1007/978-3-658-29267-6_21

Abstract

Mammography is using low-energy X-rays to screen the human breast and is utilized by radiologists to detect breast cancer. Typically radiologists require a mammogram with impeccable image quality for an accurate diagnosis. In this study, we propose a deep learning method based on Convolutional Neural Networks (CNNs) for mammogram denoising to improve the image quality. We first enhance the noise level and employ Anscombe Transformation (AT) to transform Poisson noise to white Gaussian noise. With this data augmentation, a deep residual network is trained to learn the noise map of the noisy images. We show, that the proposed method can remove not only simulated but also real noise. Furthermore, we also compare our results with state-of-the-art denoising methods, such as BM3D and DNCNN. In an early investigation, we achieved qualitatively better mammogram denoising results.

Authors with CRIS profile

Sulaiman Vesal Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens AG DE Germany (DE)

How to cite

APA:

Eckert, D., Vesal, S., Ritschl, L., Kappler, S., & Maier, A. (2020). Deep learning-based denoising of mammographic images using physics-driven data augmentation. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Informatik aktuell (pp. 94-100). Berlin, DE: Springer.

MLA:

Eckert, Dominik, et al. "Deep learning-based denoising of mammographic images using physics-driven data augmentation." Proceedings of the International workshop on Algorithmen - Systeme - Anwendungen, 2020, Berlin Ed. Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm, Springer, 2020. 94-100.

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