Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Lu Liu

1PUBLICATIONS
9CO-AUTHORS
Deep learning
Featured researcher

Get your video featured.

JoVEPublish with JoVE
Featured researcher

Get your video featured.

JoVEPublish with JoVE
Journal

Publications (1)

Sort by Publication Date:
|Mar 10, 2022
A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning.

Ruqian Hao, Lin Liu, Jing Zhang

Pageof 1

Frequent Collaborators

1 joint publications

Ruqian Hao

1 joint publications

Lin Liu

1 joint publications

Jing Zhang

1 joint publications

Xiangzhou Wang

1 joint publications

Juanxiu Liu

1 joint publications

Xiaohui Du

1 joint publications

Wen He

1 joint publications

Jicheng Liao

1 joint publications

Yuanying Mao

Frequent Collaborators

1 joint publications

Ruqian Hao

1 joint publications

Lin Liu

1 joint publications

Jing Zhang

1 joint publications

Xiangzhou Wang

Top Related Videos

Platform for Quantitative Detection of Endometrial Immune Cells Based on Immunohistochemistry and Digital Image Analysis
07:46

Platform for Quantitative Detection of Endometrial Immune Cells Based on Immunohistochemistry and Digital Image Analysis

Published on : Oct 13, 2023

1.4K
See more related videos

Top Related Videos

Platform for Quantitative Detection of Endometrial Immune Cells Based on Immunohistochemistry and Digital Image Analysis
07:46

Platform for Quantitative Detection of Endometrial Immune Cells Based on Immunohistochemistry and Digital Image Analysis

Published on : Oct 13, 2023

1.4K
See more related videos