The impact of pre-processing techniques on deep learning breast image segmentation.

ABSTRACT: Breast cancer is one of the most common forms of cancer worldwide, making breast imaging a critical area for developing and evaluating Deep Learning methods. In this study, we investigate how different pre-processing techniques influence model performance in breast image segmentation. Pre-processing is a crucial step in the Deep Learning pipeline that directly impacts model performance, yet studies on its role in medical imaging remain limited. We assess the influence of different pre-processing techniques on a U-Net segmentation model applied to two breast public imaging datasets: CBIS-DDSM and Duke-Breast-Cancer-MRI. We systematically explored commonly used methods, including pixel intensity normalization, spacing harmonization, resizing/padding, and orientation standardization. Two proc

SUBMITTER: Catarino J 

PROVIDER: S-EPMC12789679 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature