Deep Learning Methods for Mitosis Detection in Breast Cancer Histopathological Images: A Comprehensive Review-Reference-Cited by-同舟云学术

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Deep Learning Methods for Mitosis Detection in Breast Cancer Histopathological Images: A Comprehensive Review

Published:2020 Issue: Volume: Page:279-306
ISSN:0302-9743
Container-title:Artificial Intelligence and Machine Learning for Digital Pathology
language:
Short-container-title:

Author:

Dif Nassima^ORCID,Elberrichi Zakaria^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-50402-1_17

Reference102 articles.

1. Agoff, S.N., Lawton, T.J.: Papillary lesions of the breast with and without atypical ductal hyperplasia: can we accurately predict benign behavior from core needle biopsy? Am. J. Clin. Pathol. 122(3), 440–443 (2004)

2. Lecture Notes in Computer Science;SU Akram,2018

3. Al-Janabi, S., van Slooten, H.J., Visser, M., Van Der Ploeg, T., Van Diest, P.J., Jiwa, M.: Evaluation of mitotic activity index in breast cancer using whole slide digital images. PLoS One 8(12), e82576 (2013)

4. Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., Navab, N.: AggNet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans. Med. Imaging 35(5), 1313–1321 (2016)

5. Albayrak, A., Bilgin, G.: Mitosis detection using convolutional neural network based features. In: IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 000335–000340. IEEE (2016)

Cited by 2 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. Merging nucleus datasets by correlation-based cross-training;Medical Image Analysis;2023-02

2. Mitosis Detection in Breast Cancer with Deep Learning: A New Approach;2022 International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE);2022-11-26

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