Mr. Joseph Cox Profile

Joseph Cox

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 | 16 March 2020 Paper

Hyperparameter selection for ResNet classification of malignancy from thyroid ultrasound images

Joseph Cox, Sydney Rubin, Joe Adams, Carina Pereira, Manjiri Dighe, Adam Alessio

Proceedings Volume 11314, 1131447 (2020) https://doi.org/10.1117/12.2550531

KEYWORDS: Data modeling, RGB color model, Ultrasonography, Performance modeling, Image classification, Elastography, Biopsy, Fine needle aspiration, Optimization (mathematics)

Read Abstract +

Thyroid nodules are extremely common, with a prevalence of up to 68% in adults. Ultrasound imaging is usually performed to detect and evaluate thyroid nodules for malignancy. Many patients undergo follow-up biopsy in the form of fine-needle aspiration (FNA) to determine if a nodule is malignant or benign, although most nodules are benign. In order to reduce the number of unnecessary FNAs, radiologists will often use classification systems such as Thyroid Imaging, Reporting, and Data System (TI-RADS) to provide risk stratification and a recommendation regarding whether FNA is necessary. This scoring is both subjective and time-consuming, leading to discrepancies between radiologists and recommendations that can be inaccurate. We hypothesize that a machine learned classifier can be identified with accurate and generalizable performance, potentially offering more consistent results than manual evaluation. We created a network from two ResNet-50 branches accepting two inputs, shear-wave elastography and B-mode ultrasound images. We performed a grid search to determine the optimal hyperparameters for our model, resulting in a network that predicted malignancy of nodules with 88.7% accuracy and an AUC of 0.91. Along with identifying the training hyperparameters with optimal classification accuracy, the grid search also allowed us to select training parameters that led to more generalizable model performance on test data sets. These initial performance results suggest that our model offers a promising strategy for thyroid nodule classification and a strategy to help identify more generalizable models.

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