CalAttnMix: An instance-attention based data augmentation method for calcification detection in mammograms
Author:
Funder
Science and Technology Commission of Shanghai Municipality
National Natural Science Foundation of China
Fudan University
Publisher
Elsevier BV
Subject
Health Informatics,Signal Processing,Biomedical Engineering
Reference39 articles.
1. Sun, Z., Jiang, H., Ma, L., Yu, Z., Xu, H., 2022. Transformer based multi-view network for mammographic image classification. In: 2022 Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 46–54.
2. Breast cancer screening outcomes in women ages 40–49: clinical experience with service screening using modern mammography;Sickles;JNCI Monographs,1997
3. F. Zhang, L. Luo, X. Sun, Z. Zhou, X. Li, Y. Yu, Y. Wang, Cascaded generative and discriminative learning for microcalcification detection in breast mammograms, In: 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 12578-12586.
4. Detection and classification of microcalcifications in mammograms images using difference filter and Yolov4 deep learning model;Yurdusev;Biomed. Signal Process. Control,2023
5. DU-Net: Convolutional network for the detection of arterial calcifications in mammograms;AlGhamdi;IEEE Trans. Med. Imaging,2020
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