Review Article| Volume 194, P19-35, April 2018

Digital image analysis in breast pathology—from image processing techniques to artificial intelligence

  • Stephanie Robertson
    Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden

    Department of Clinical Pathology and Cytology, Karolinska University Laboratory, Stockholm, Sweden
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  • Hossein Azizpour
    School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden

    Science for Life Laboratory, Stockholm, Sweden
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  • Kevin Smith
    School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden

    Science for Life Laboratory, Stockholm, Sweden
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  • Johan Hartman
    Reprint requests:Department of Oncology-Pathology, Karolinska Institutet, CCK, SE-17176 Stockholm, Sweden;
    Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden

    Department of Clinical Pathology and Cytology, Karolinska University Laboratory, Stockholm, Sweden

    Stockholm South General Hospital, Stockholm, Sweden
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Published:November 07, 2017DOI:
      Breast cancer is the most common malignant disease in women worldwide. In recent decades, earlier diagnosis and better adjuvant therapy have substantially improved patient outcome. Diagnosis by histopathology has proven to be instrumental to guide breast cancer treatment, but new challenges have emerged as our increasing understanding of cancer over the years has revealed its complex nature. As patient demand for personalized breast cancer therapy grows, we face an urgent need for more precise biomarker assessment and more accurate histopathologic breast cancer diagnosis to make better therapy decisions. The digitization of pathology data has opened the door to faster, more reproducible, and more precise diagnoses through computerized image analysis. Software to assist diagnostic breast pathology through image processing techniques have been around for years. But recent breakthroughs in artificial intelligence (AI) promise to fundamentally change the way we detect and treat breast cancer in the near future. Machine learning, a subfield of AI that applies statistical methods to learn from data, has seen an explosion of interest in recent years because of its ability to recognize patterns in data with less need for human instruction. One technique in particular, known as deep learning, has produced groundbreaking results in many important problems including image classification and speech recognition. In this review, we will cover the use of AI and deep learning in diagnostic breast pathology, and other recent developments in digital image analysis.


      DIA (digital image analysis), AI (artificial intelligence), H&E (hematoxylin and eosin), IHC (immunohistochemistry), ISH (in situ hybridization), DCIS (ductal carcinoma in situ), ER (estrogen receptor α), PR (progesterone receptor), HER2 (human epidermal growth factor receptor 2), DNA (deoxyribonucleic acid), mRNA (messenger ribonucleic acid), WSI (whole-slide imaging), CAD (computer-aided diagnosis), RF (random forest), SVM (support vector machine), MIL (multiple instance learning), ConvNet (convolutional network)
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