Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation-Reference-Cited by-同舟云学术

Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation

Published:2022 Issue: Volume: Page:3-13
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
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Author:

Chen Yuanhong,Wang Hu,Wang Chong,Tian Yu,Liu Fengbei,Liu Yuyuan,Elliott Michael,McCarthy Davis J.,Frazer Helen,Carneiro Gustavo

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-16437-8_1

Reference32 articles.

1. Carneiro, G., Nascimento, J., Bradley, A.P.: Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Trans. Med. Imaging 36(11), 2355–2365 (2017)

2. Dembrower, K., Lindholm, P., Strand, F.: A multi-million mammography image dataset and population-based screening cohort for the training and evaluation of deep neural networks-the cohort of screen-aged women (CSAW). J. Digit. Imaging 33(2), 408–413 (2020)

3. Dosovitskiy, A., et al.: An image is worth $$16 \times 16$$ words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

4. Frazer, H.M., Qin, A.K., Pan, H., Brotchie, P.: Evaluation of deep learning-based artificial intelligence techniques for breast cancer detection on mammograms: results from a retrospective study using a BreastScreen Victoria dataset. J. Med. Imaging Radiat. Oncol. 65(5), 529–537 (2021)

5. Freeman, K., et al.: Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. bmj 374 (2021)

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