Mr. Zhengyao Peng Profile

Zhengyao Peng

at Institute of Automation CAS

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 4 April 2022 Poster + Presentation + Paper

A deep learning-based computational prediction model for characterizing cellular biomarker distribution in tumor microenvironment

Zhengyao Peng, Chang Bian, Yang Du, Jie Tian

Proceedings Volume 12039, 120391H (2022) https://doi.org/10.1117/12.2610640

KEYWORDS: Tumors, Data modeling, Performance modeling, Image segmentation, Convolution, Cancer, Binary data, Image analysis, Biological research, Multiplexing

Read Abstract +

Evaluation of cancer cell and immune cell distribution in tumor microenvironment (TME) is one of the most important factors for guiding cancer immunotherapy and assessing therapeutic response. Multiplexed immunohistochemistry (mIHC) is often used to obtain the different types of cellular biomarker expression and distribution information in TME, but mIHC is limited by time-consuming and cost-intensive, and pathologists’ objectives etc. In this work, we proposed a deep learning-based modified U-Net (m-Unet), by replacing the original convolution sub-module with a modified block to predict the distribution of several typical cellular biomarkers’ expression and distribution information in TME. We have demonstrated that our model can be trained in both fully supervised and semi-supervised manners. The model can extract segmentation information from Hematoxylin and Eosin (H&E) images, and predict the cellular biomarker distributions including panCK for colon cancer cells, CD3 and CD20 for tumor infiltrating lymphocytes (TILs) and DAPI for nucleus. We have demonstrated that our model can be trained in both fully supervised and semi-supervised manners and. the performance of the m-Unet is better than the U-Net in this work. The optimal prediction accuracy of m-Unet is 88.3% on the test dataset. In general, this model possesses the potential to assist the clinical TME analysis.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years