Applying graph convolution neural network in digital breast tomosynthesis for cancer classification-Reference-Cited by-同舟云学术

Applying graph convolution neural network in digital breast tomosynthesis for cancer classification

Published:2022-08-07 Issue: Volume: Page:
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Container-title:Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
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Author:

Bai Jun¹,Jin Annie¹,Jin Andre¹,Wang Tianyu¹,Yang Clifford¹,Nabavi Sheida¹

Affiliation:

1. University of Connecticut

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3535508.3545549

Reference41 articles.

1. Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

2. Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review

3. Cătălina Cangea , Petar Veličković , Nikola Jovanović , Thomas Kipf , and Pietro Liò . 2018. Towards sparse hierarchical graph classifiers. arXiv preprint arXiv:1811.01287 ( 2018 ). Cătălina Cangea, Petar Veličković, Nikola Jovanović, Thomas Kipf, and Pietro Liò. 2018. Towards sparse hierarchical graph classifiers. arXiv preprint arXiv:1811.01287 (2018).

4. Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study

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