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Review
. 2020 Feb:49:267-273.
doi: 10.1016/j.breast.2019.12.007. Epub 2019 Dec 19.

Artificial intelligence in digital breast pathology: Techniques and applications

Asmaa Ibrahim  1 , Paul Gamble  2 , Ronnachai Jaroensri  2 , Mohammed M Abdelsamea  3 , Craig H Mermel  2 , Po-Hsuan Cameron Chen  2 , Emad A Rakha  4
Affiliations
Review

Artificial intelligence in digital breast pathology: Techniques and applications

Asmaa Ibrahim et al. Breast. 2020 Feb.

Abstract

Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis. However, emerging knowledge about the complex nature of cancer and the availability of tailored therapies have exposed opportunities for improvements in diagnostic precision. In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumours. In this article, we cover the current and prospective uses of AI in digital pathology for breast cancer, review the basics of digital pathology and AI, and outline outstanding challenges in the field.

Keywords: (Artificial intelligence); (Deep learning); (Machine learning); (Whole slide image); AI; Applications; Breast cancer; Breast pathology; DL; Digital; ML; Pathology; WSI.

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