Assessment of an unsupervised denoising approach based on Noise2Void in digital mammography

Abstract Full-field digital mammography (FFDM) is the most common imaging technique for breast cancer screening programs. Still, it is limited by noise from quantum effects, electronic issues, and X-ray scattering, affecting the image quality. Traditional denoising methods based on filters and transformations perform poorly due to the complex, tissue-dependent nature of noise, while supervised deep learning methods require extensive, often unavailable datasets with paired noisy and noiseless images. Consequently, unsupervised denoising methods, which do not require clean images as ground truth, are gaining attention. However, their application to FFDM is poorly explored. This study investigates the use of Noise2Void (N2V), an unsupervised denoising approach adapted to digital mammography images for the first time. N2V employs blind spot masking to remove noise without requiring noiseless images. The method was assessed using different metrics on real clinical images and artificially noised images: contrast-to-noise ratio (CNR), and structural similarity index (SSIM). A qualitative evaluation was also made based on a questionnaire provided to radiologists. The results show that evaluated metrics increase on N2V images; these results are comparable with traditional methods. Despite showing quantitative performance comparable to traditional methods, N2V retains potential for clinical application as a flexible, annotation-free approach for retrospective, low-dose mammography imaging.