Image-To-Image Translation Network Model Development for Automatic Contrast Enhancement on Low-Dose Mammographic Images
Year: 2025
Type: conference-paper
Abstract: This study presents an Image-to-Image neural network model designed to enhance the contrast of low-dose mammographic images, aiming to improve diagnostic quality while adhering to the ALARA principle. Using a dataset of breast phantom images acquired at varying radiation doses, the model leverages generative adversarial networks (GANs) to reconstruct high-contrast images from low-dose scans. Performance evaluation based on structural similarity (SSIM) and Carneiro Contrast Index (CCI) demonstrates that the model effectively approximates high-dose image quality, supporting its potential role in reducing radiation exposure without compromising diagnostic accuracy.—
Authors: Brenha, Anna Carolina Falanga, Dinalli, Gian Lucca Zancan, Schiabel, Homero, Carneiro, Pedro Cunha, Patrocínio, Ana Claudia
Institutions: Universidade Federal de Uberlândia, Universidade de São Paulo
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7091286363
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