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

SUBMITTER: Angelone F 

PROVIDER: S-EPMC12518800 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature