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Date Published: 08/06/2021
Abstract Views: 1036
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DOI: https://doi.org/10.51298/vmj.v500i1.290
Issue
Vol. 500 No. 1 (2021)
Section
Các bài báo
How to Cite
Văn Tú, Đào ., Khắc Dũng, N. ., Thị Oanh, B. ., Lê Hiệp, N. ., Đức Hoàn, V. ., Hữu Dũng, Đặng ., Văn Chủ, N. ., Văn Giang, B. ., & Văn Tờ, T. . (2021). OVERVIEW OF APPLICATION RESEARCH OF ARTIFICIAL INTELLIGENCE ON BREAST CANCER DIAGNOSIS BASED ON DIGITAL PATHOLOGY IMAGES. Vietnam Medical Journal, 500(1). https://doi.org/10.51298/vmj.v500i1.290

OVERVIEW OF APPLICATION RESEARCH OF ARTIFICIAL INTELLIGENCE ON BREAST CANCER DIAGNOSIS BASED ON DIGITAL PATHOLOGY IMAGES

Đào Văn Tú, Nguyễn Khắc Dũng, Bùi Thị Oanh, Nguyễn Lê Hiệp, Vũ Đức Hoàn, Đặng Hữu Dũng, Nguyễn Văn Chủ, Bùi Văn Giang, Tạ Văn Tờ

Main Article Content

Abstract

PATHOLOGY IMAGES


Breast cancer is the most common cancer in women and the leading cause of cancer deaths worldwide. The essential basisfor breast cancer treatment is histopathology, which determines the direction of treatment and prognosis of the disease. Advances in artificial intelligence (AI) together with the use of digital pathology have presented a promising approach in breast cancer diagnosis and classification, meeting the real needs clinical practice. In this article, we provide an overview of AI applications in breast cancer diagnostics based on digital pathology images of the disease, and proposethe potentials in application in Vietnam.

Article Details

Keywords

Breast cancer, artificial interligence, pathology image

References

1. Prewitt J.M. và Mendelsohn M.L. (1966). The analysis of cell images. Ann N Y Acad Sci, 128(3), 1035–1053.
2. Mukhopadhyay S., Feldman M.D., Abels E. và cộng sự (2018). Whole Slide Imaging Versus Microscopy for Primary Diagnosis in Surgical Pathology: A Multicenter Blinded Randomized Noninferiority Study of 1992 Cases (Pivotal Study). Am J Surg Pathol, 42(1), 39–52.
3. Williams B.J., DaCosta P., Goacher E. và cộng sự (2017). A Systematic Analysis of Discordant Diagnoses in Digital Pathology Compared With Light Microscopy. Arch Pathol Lab Med, 141(12), 1712–1718.
4. Ozkan T.A., Eruyar A.T., Cebeci O.O. và cộng sự (2016). Interobserver variability in Gleason histological grading of prostate cancer. Scand J Urol, 50(6), 420–424.
5. Cruz-Roa A., Gilmore H., Basavanhally A. và cộng sự (2017). Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent. Sci Rep, 7, 46450.
6. Han Z., Wei B., Zheng Y. và cộng sự (2017). Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model. Sci Rep, 7(1), 1–10.
7. Ehteshami Bejnordi B., Veta M., Johannes van Diest P. và cộng sự. (2017). Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA, 318(22), 2199–2210.
8. Veta M., Heng Y.J., Stathonikos N. và cộng sự. (2019). Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge. Med Image Anal, 54, 111–121.
9. Veta M., Kornegoor R., Huisman A. và cộng sự. (2012). Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer. Mod Pathol Off J U S Can Acad Pathol Inc, 25(12), 1559–1565.