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.
Abbreviations:
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)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: November 07, 2017
Accepted:
October 30,
2017
Received in revised form:
October 28,
2017
Received:
September 1,
2017
Identification
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© 2017 Elsevier Inc. All rights reserved.
