Mr. Vincent Wang Profile

Vincent Wang

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 | 15 February 2021 Poster + Presentation + Paper

A comparison study of deep learning designs for improving low-dose CT denoising

Vincent Wang, Alice Wei, Jiaxing Tan, Siming Lu, Weiguo Cao, Yongfeng Gao

Proceedings Volume 11596, 115962N (2021) https://doi.org/10.1117/12.2582322

KEYWORDS: Denoising, Network architectures, Computed tomography

Read Abstract +

Low-dose denoising is an effective method that utilizes the power of CT for screening while avoiding high radiation exposure. Several research work has reported the feasibility of deep learning based denoising, but none of them have explored the influence of different network designs on the denoising performance. In this work, we explored the impact of three commonly adapted network design concepts in denoising: (1) Network structure, (2) Residual learning, and (3) Training loss. We evaluated the network performance using the dataset containing 76 real patient scans from Mayo Clinic Low-dose CT dataset. Experimental results demonstrated that residual blocks and residual learning are recommended to be utilized in design, while pooling is not recommended. In addition, among the classical training losses, the mean absolute error (L1) loss outperforms the mean squared error (MSE) loss.

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