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Survey of deep learning in breast cancer image analysis

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Abstract

Computer-aided image analysis for better understanding of images has been time-honored approaches in the medical computing field. In the conventional machine learning approach, the domain experts in medical images are mandatory for image annotation that subsequently to be used for feature engineering. However, in deep learning, a big jump has been made to help the researchers do segmentation, feature extraction, classification, and detection from raw medical images obtained using digital breast tomosynthesis, digital mammography, magnetic resonance imaging, and ultrasound imaging modalities. As a result, deep learning (DL) has gained a state-of-the-art in many application areas, for example, breast cancer image analysis. In this survey paper, we reviewed the most common breast cancer imaging modalities, public, most cited and recently updated breast cancer databases, histopathological based breast cancer image analysis, and DL application types in medical image analysis. We finally conclude by pointing out the research gaps to be addressed in the future.

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Acknowledgements

The corresponding author would like to thank the Ethiopian Ministry of Education (MoE) and the Deutscher Akademischer Auslandsdienst (DAAD) for funding this research work (Funding number 57162925).

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Authors and Affiliations

  1. Institute of Neural Information Processing, University of Ulm, 89069, Ulm, Germany

    Taye Girma Debelee & Friedhelm Schwenker

  2. Department of Computer Engineering, Addis Ababa Science and Technology University, P.O. Box 120611, Addis Ababa, Ethiopia

    Taye Girma Debelee & Dereje Yohannes

  3. HAWK University of Applied Sciences and Arts, 37085, Göttingen, Germany

    Achim Ibenthal

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  1. Taye Girma Debelee
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Debelee, T.G., Schwenker, F., Ibenthal, A. et al. Survey of deep learning in breast cancer image analysis. Evolving Systems 11, 143–163 (2020). https://doi.org/10.1007/s12530-019-09297-2

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