Lightweight U-Net for High-Resolution Breast Imaging

2020/11/27 by Mickael Tardy, Tardy, Mickael, Diana Mateus +1
Computer Science · Medicine · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Medical Image Segmentation Techniques #Radiomics and Machine Learning in Medical Imaging

paper · pdf · doi:10.48550/arxiv.2011.13698

openalex publication_date 2020/11/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
• Attention U-Net: Learning Where to Look for the Pancreas
• AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms
• Cancer statistics, 2019

Related

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• Using convolutional neural networks for the classification of breast cancer images
• Deep Convolutional Neural Networks: A survey of the foundations, selected improvements, and some current applications
• An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
• Convolutional Neural Networks for Breast Cancer Screening: Transfer Learning with Exponential Decay