Dr. Chen Li Profile

Dr. Chen Li

at Northeastern Univ

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 | 13 March 2019 Paper

Predicting unnecessary nodule biopsy for a small lung cancer screening dataset by less-abstractive deep features

Fangfang Han, Linkai Yan, Chen Li, Shouliang Qi, William Moore, Zhengrong Liang, Wei Qian

Proceedings Volume 10950, 109503Q (2019) https://doi.org/10.1117/12.2512449

KEYWORDS: Biopsy, Lung cancer, Computed tomography, Medical imaging, Image classification, Convolutional neural networks, Surgery, Lung, Machine learning, Cancer

Read Abstract +

Screening lung cancer by computed tomography (CT) has shown great benefit for early cancer detection, but requires a great effort to eliminate the associated false detection, where the biopsy option costs most among other eliminating options. Therefore it is significant to study lung cancer through image analysis to decrease biopsy tests. However, it is extremely difficult to get enough data with biopsy reports from hospital for machine learning study in a short period. So this study aims to explore machine transfer learning innovations to predict unnecessary biopsies from a very small dataset of pathologically proven nodule CT images. To overcome the problem of big data requirement of the CNN architecture (such as VGG used in this study), we used the parameters trained by ImageNet as the initial features. Then we put part of the labeled pulmonary nodule dataset with the ground truth into the training dataset to fine-tune the parameters of different architectures. Fifty repetitions of the cross validation method of two-thirds training and one-third testing are used to measure the efficiency of different deep transfer learning architectures. Through the classification results shown in ROC curves and AUC values, we find that deep features transferred from natural images can enhance 0.1663 more than the traditional machine learning method based on texture features extracted from gray images directly. And our improved VGG architecture with 8 layers for achieving less-abstractive features can obtain 0.1081 better performance than the more-abstractive ones on the recognition of malignant nodules.

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