Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Konstantin Dmitriev , Arie E. Kaufman , Ammar A. Javed , Ralph H. Hruban , Elliot K. Fishman , Anne Marie Lennon , Joel H. Saltz

ABSTRACT

There are many different types of pancreatic cysts. These range from completely benign to malignant, and identifying the exact cyst type can be challenging in clinical practice. This work describes an automatic classification algorithm that classifies the four most common types of pancreatic cysts using computed tomography images. The proposed approach utilizes the general demographic information about a patient as well as the imaging appearance of the cyst. It is based on a Bayesian combination of the random forest classifier, which learns subclass-specific demographic, intensity, and shape features, and a new convolutional neural network that relies on the fine texture information. Quantitative assessment of the proposed method was performed using a 10-fold cross validation on 134 patients and reported a classification accuracy of 83.6%. More... »

PAGES

150-158

References to SciGraph publications

Book

TITLE

Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017

ISBN

978-3-319-66178-0
978-3-319-66179-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-66179-7_18

DOI

http://dx.doi.org/10.1007/978-3-319-66179-7_18

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091431981

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29881827


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