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Computational methods for automated mitosis detection in histopathology images: A review

  • Published:2021-01 Issue:1 Volume:41 Page:64-82
  • ISSN:0208-5216
  • Container-title:Biocybernetics and Biomedical Engineering
  • language:en
  • Short-container-title:Biocybernetics and Biomedical Engineering

Author:

Mathew TojoORCID,Kini Jyoti R.ORCID,Rajan Jeny

Publisher

Elsevier BV

Subject

Biomedical Engineering

Reference132 articles.

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2. Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries;Bray;CA: Cancer J Clin,2018

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1. A comprehensive review of tubule formation in histopathology images: advancement in tubule and tumor detection techniques;Artificial Intelligence Review;2024-09-11

2. Mitosis detection, fast and slow: Robust and efficient detection of mitotic figures;Medical Image Analysis;2024-05

3. Deep Learning ResNet-50 Framework for Automatic Early Breast Cancer Diagnosis;2024 International Conference on Communication, Computing and Internet of Things (IC3IoT);2024-04-17

4. The 1000 Mitoses Project: A Consensus-Based International Collaborative Study on Mitotic Figures Classification;International Journal of Surgical Pathology;2024-04-16

5. Analysis of U-Net and Modified VGG16 Technique for Mitosis Identification in Histopathology Images;2024 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET);2024-03-21
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