Deep Learning in Mammography and Breast Histology, an Overview and Future Trends

Azam Hamidinekoo, Erika R. E. Denton, Yambu Andrik Rampun, Kate Honnor, Reyer Zwiggelaar

Research output: Contribution to journalArticlepeer-review

205 Citations (SciVal)
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Abstract

Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems. Considering the importance of breast cancer worldwide and the promising results reported by deep learning based methods in breast imaging, an overview of the recent state-of-the-art deep learning based CAD systems developed for mammography and breast histopathology images is presented. In this study, the relationship between mammography and histopathology phenotypes is described, which takes biological aspects into account. We propose a computer based breast cancer modelling approach: the Mammography-Histology-Phenotype-Linking-Model, which develops a mapping of features/phenotypes between mammographic abnormalities and their histopathological representation. Challenges are discussed along with the potential contribution of such a system to clinical decision making and treatment management
Original languageEnglish
Pages (from-to)45-67
Number of pages23
JournalMedical Image Analysis
Volume47
Early online date26 Mar 2018
DOIs
Publication statusPublished - 01 Jul 2018

Keywords

  • mammography
  • breast histopathology
  • computer aided diagnosis
  • deep learning

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