Generating Full-Field Digital Mammogram From Digitized Screen-Film Mammogram for Breast Cancer Screening With High-Resolution Generative Adversarial Network.

ABSTRACT:

Purpose

Developing deep learning algorithms for breast cancer screening is limited due to the lack of labeled full-field digital mammograms (FFDMs). Since FFDM is a new technique that rose in recent decades and replaced digitized screen-film mammograms (DFM) as the main technique for breast cancer screening, most mammogram datasets were still stored in the form of DFM. A solution for developing deep learning algorithms based on FFDM while leveraging existing labeled DFM datasets is a generative algorithm that generates FFDM from DFM. Generating high-resolution FFDM from DFM remains a challenge due to the limitations of network capacity and lacking GPU memory.

Method

In this study, we developed a deep-learning-based generative algorithm, HRGAN, to generate synthesized FFDM (S

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC9105019 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature