Artificial intelligence for digital and computational pathology

被引：85

作者：

Song, Andrew H. ^{[1
,2
,3
,4
]}

Jaume, Guillaume ^{[1
,2
,3
,4
]}

Williamson, Drew F. K. ^{[1
,2
,3
,4
]}

Lu, Ming Y. ^{[1
,2
,3
,4
,5
]}

Vaidya, Anurag ^{[1
,2
,3
,4
,6
]}

Miller, Tiffany R. ^{[1
]}

Mahmood, Faisal ^{[1
,2
,3
,4
,7
]}

机构：

[1] Harvard Med Sch, Brigham & Womens Hosp, Dept Pathol, Boston, MA 02115 USA

[2] Harvard Med Sch, Dept Pathol, Massachusetts Gen Hosp, Boston, MA 02115 USA

[3] Broad Inst Harvard & MIT, Canc Program, Cambridge, MA 02142 USA

[4] Dana Farber Canc Inst, Data Sci Program, Boston, MA 02215 USA

[5] MIT, Dept Elect Engn & Comp Sci, Cambridge, MA USA

[6] MIT, Harvard MIT Div Hlth Sci & Technol, Cambridge, MA USA

[7] Harvard Univ, Harvard Data Sci Initiat, Cambridge, MA 02138 USA

来源：

NATURE REVIEWS BIOENGINEERING | 2023年 / 1卷 / 12期

关键词：

GENE MUTATION HETEROGENEITY; PROSTATE-CANCER; NEURAL-NETWORK; DEEP; SEGMENTATION; HEALTH; IMAGES; REPRESENTATION; MEDICINE; BIOPSIES;

D O I：

10.1038/s44222-023-00096-8

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of these artificial intelligence-based systems are now getting approved to assist clinical diagnosis; however, technical barriers remain for their widespread clinical adoption and integration as a research tool. This Review consolidates recent methodological advances in computational pathology for predicting clinical end points in whole-slide images and highlights how these developments enable the automation of clinical practice and the discovery of new biomarkers. We then provide future perspectives as the field expands into a broader range of clinical and research tasks with increasingly diverse modalities of clinical data.

引用

页码：930 / 949

页数：20

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