Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation
This addresses image quality issues in mammography for radiologists, but it is incremental as it builds on existing deep learning denoising techniques.
The study tackled mammogram denoising to improve image quality for breast cancer detection, achieving qualitatively better results compared to state-of-the-art methods like BM3D and DNCNN.
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.