Lightweight U-Net for High-Resolution Breast Imaging
Mickael Tardy, Diana Mateus

TL;DR
This paper introduces a lightweight U-Net architecture designed for high-resolution breast imaging, balancing segmentation accuracy with computational efficiency for breast cancer screening.
Contribution
It proposes a simplified U-Net model optimized for high-resolution breast images, improving efficiency without sacrificing significant accuracy.
Findings
Achieved effective segmentation with reduced computational load.
Demonstrated potential for real-time breast cancer screening applications.
Balanced model complexity and performance in high-resolution imaging.
Abstract
We study the fully convolutional neural networks in the context of malignancy detection for breast cancer screening. We work on a supervised segmentation task looking for an acceptable compromise between the precision of the network and the computational complexity.
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Taxonomy
TopicsAI in cancer detection · Radiomics and Machine Learning in Medical Imaging · Medical Image Segmentation Techniques
