M&M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector-Reference-Cited by-同舟云学术

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M&M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector

Published:2023 Issue: Volume: Page:778-788
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Truong Vu Yen Nhi,Guo Dan,Taha Ahmed,Su Jason,Matthews Thomas Paul

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-43904-9_75

Reference33 articles.

1. Agarwal, R., Diaz, O., Lladó, X., Yap, M.H., Martí, R.: Automatic mass detection in mammograms using deep convolutional neural networks. J. Med. Imaging 6(3), 031409 (2019)

2. Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: CVPR (2018)

3. Campanini, R., et al.: A novel featureless approach to mass detection in digital mammograms based on support vector machines. Phys. Med. Bio. 49(6), 961 (2004)

4. Duffy, S.W., et al.: Mammography screening reduces rates of advanced and fatal breast cancers: results in 549,091 women. Cancer 126(13), 2971–2979 (2020)

5. Eltonsy, N.H., Tourassi, G.D., Elmaghraby, A.S.: A concentric morphology model for the detection of masses in mammography. IEEE Trans. Med. Imaging 26(6), 880–889 (2007)

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. HybridMammoNet: A Hybrid CNN-ViT Architecture for Multi-view Mammography Image Classification;2024 IEEE 12th International Symposium on Signal, Image, Video and Communications (ISIVC);2024-05-21

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