Mr. Felipe Torres Figueroa Profile

Felipe Torres Figueroa

SPIE Involvement:

Author

Publications (2)

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 | 3 January 2020 Paper

An empirical study on global bone age assessment

Felipe Torres, Cristina González, María Camila Escobar, Laura Daza, Gustavo Triana, Pablo Arbeláez

Proceedings Volume 11330, 113300E (2020) https://doi.org/10.1117/12.2542431

KEYWORDS: Bone, X-ray imaging, X-rays, Machine learning, Convolutional neural networks, Network architectures

Read Abstract +

Bone Age Assessment (BAA) is a task performed by physicians to estimate the skeletal development of a pediatric patient. Tipically physicians perform this exam by doing a manual analysis of the X-ray image of the non-dominant hand of a child, either by taking the image as a whole or paying attention to certain anatomical Regions Of Interest (ROIs). Over the years, several datasets have been proposed in order to generate automated methods to perform this task. Most notably, in 2017 the Radiological Society of North America (RSNA)1 created the Pediatric Bone Age Challenge, which encouraged the development of machine learning approaches for this task. In this paper, we present GPNet a convolutional neural network capable of performing BAA precisely and effectively by analyzing the whole hand in a single forward pass. We train GPNet using the training data available from the dataset created in the RSNA challenge and evaluate our method using the validation set. We use the testing set to compare our performance with the current state-of-the-art and find that GPNet significantly outperforms previous methods. During our architecture search we perform several experiments to demonstrate the effect of different layers, proving that some blocks do not contribute to the performance of the network, but instead they affect it. As a result, we are able to develop a method that reduces the number of trainable parameters by nearly 82.15 M in comparison to the state-of-the-art, while improving the performance.

Proceedings Article | 17 November 2017 Paper

Bone age detection via carpogram analysis using convolutional neural networks

Felipe Torres, María Alejandra Bravo, Emmanuel Salinas, Gustavo Triana, Pablo Arbeláez

Proceedings Volume 10572, 1057217 (2017) https://doi.org/10.1117/12.2285949

KEYWORDS: Bone, Binary data, Diagnostics, Convolutional neural networks, X-rays, Databases, Data modeling, Iron, X-ray imaging, Image classification

Read Abstract +

Bone age assessment is a critical factor for determining delayed development in children, which can be a sign of pathologies such as endocrine diseases, growth abnormalities, chromosomal, neurological and congenital disorders among others. In this paper we present BoneNet, a methodology to assess automatically the skeletal maturity state in pediatric patients based on Convolutional Neural Networks. We train and evaluate our algorithm on a database of X-Ray images provided by the hospital Fundacion Santa Fe de Bogot ´ a with around 1500 images of patients between the ages 1 to 18. ´ We compare two different architectures to classify the given data in order to explore the generality of our method. To accomplish this, we define multiple binary age assessment problems, dividing the data by bone age and differentiating the patients by their gender. Thus, exploring several parameters, we develop BoneNet. Our approach is holistic, efficient, and modular, since it is possible for the specialists to use all the networks combined to determine how is the skeletal maturity of a patient. BoneNet achieves over 90% accuracy for most of the critical age thresholds, when differentiating the images between over or under a given age.

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