Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation

Mammography uses low-energy X-rays to screen the human breast and is used by radiologists to detect breast cancer. Due to its complexity, a radiologist needs an impeccable image quality. For this reason, the possibility of using deep learning to denoise Mammograms to help radiologists detect breast cancer more easily will be examined. In this thesis, we aim to investigate and develop different deep learning methods for mammogram denoising.
A physically motivated noise model will be simulated on the ground truth images to generate training data. Thereafter the variance stabilizing Anscombe transformation is applied to create white Gaussian noise. Using these data, different network architectures are trained and examined.  For training, a novel loss function will be designed which helps to preserve fine image details crucial for breast cancer detections.
The effectiveness of this loss function is investigated, and its performance is compared again to other state-of-the-art loss functions. It can be shown that the proposed method outperforms state of the art algorithm like BM3D for mammography denoising.  Finally, it will be shown that the network is able to remove not only simulated, but also real noise.