Machine intelligence for precision oncology

doi:10.5528/wjtm.v9.i1.1

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 9, Issue 1

This Article

Academic Content and Language Evaluation of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Number of Hits and Downloads for This Article

Total Article Views (9245)

All Articles published online

The chart showing PDF series, WORD series, HTML series, Figures (1-7) series.

Item

Count

PDF

415

WORD

267

HTML

4183

Figures (1-7)

900

Sum=5765

Featured Article

The chart showing Browse series, Download series.

Item

Count

Browse

656

Download

1490

Sum=2146

Publishing Process of This Article

Item

Count

Browse

516

Download

818

Sum=1334

Mar 15, 2021 (publication date) through Nov 30, 2024

Times Cited of This Article

Times Cited (1)

Journal Information of This Article

Publication Name

World Journal of Translational Medicine

ISSN

2220-6132

Publisher of This Article

Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Editorial

World J Transl Med. Mar 15, 2021; 9(1): 1-10
Published online Mar 15, 2021. doi: 10.5528/wjtm.v9.i1.1

Full Size Figure Download Figure

Figure 4 A neural network in deep learning of digital histopathology. The input consists of digital histopathological data derived from individuals with or without cancer. The output includes classification and diagnosis of cancer as well as predicting genetic mutations and prognosis of patients with cancer.

Citation: Yee NS. Machine intelligence for precision oncology. World J Transl Med 2021; 9(1): 1-10
URL: https://www.wjgnet.com/2220-6132/full/v9/i1/1.htm
DOI: https://dx.doi.org/10.5528/wjtm.v9.i1.1