Reduction of false positives by machine learning for computer-aided detection of colonic polyps

2009 ◽  
Author(s):  
Xin Zhao ◽  
Su Wang ◽  
Hongbin Zhu ◽  
Zhengrong Liang
Keyword(s):  
Machine Learning ◽  
Colonic Polyps ◽  
False Positives ◽  
Computer Aided Detection ◽  
Computer Aided

Projection-based features for reducing false positives in computer-aided detection of colonic polyps in CT colonography

2010 ◽  
Author(s):  
Hongbin Zhu ◽  
Matthew Barish ◽  
Perry Pickhardt ◽  
Yi Fan ◽  
Erica Posniak ◽  
...  
Keyword(s):  
Ct Colonography ◽  
Colonic Polyps ◽  
False Positives ◽  
Computer Aided Detection ◽  
Computer Aided

A Model-Driven Bayesian Method for Polyp Detection and False Positive Suppression in CT Colonography Computer-Aided Detection

2012 ◽  
pp. 220-237
Author(s):  
Xujiong Ye ◽  
Greg Slabaugh
Keyword(s):  
Ct Colonography ◽  
Colonic Polyps ◽  
Volume Effect ◽  
False Positives ◽  
Detection Sensitivity ◽  
Computer Aided Detection ◽  
Polyp Detection ◽  
Model Driven ◽  
Computer Aided ◽  
Overlap Ratio

This chapter presents an automated method to identify colonic polyps and suppress false positives for Computer-Aided Detection (CAD) in CT Colonography (CTC). The method formulates the problem of polyp detection as a probability calculation through a unified Bayesian statistical approach. The polyp likelihood is modeled with a combination of shape, intensity, and location features, while also taking into account the spatial prior probability encoded by a Markov Random Field. A second principal curvature PDE provides a shape model; and partial volume effect is incorporated in the intensity model. When evaluated on a large multi-center dataset of colonic CT scans, the CAD detection performance as well as the volume overlap ratio demonstrate the potential of the proposed method. The method results in an average 24% reduction of false positives with no impact on sensitivity. The method is also applicable to generation of initial candidates for CTC CAD with high detection sensitivity and relatively lower false positives, compared to other non-Bayesian methods.

Computer-Aided Detection of Colonic Polyps Using Low-Dose CT Acquisitions

2006 ◽  
Vol 13 (9) ◽  
pp. 1062-1071 ◽  
Author(s):  
Gabriel Kiss ◽  
Stylianos Drisis ◽  
Didier Bielen ◽  
Frederik Maes ◽  
Johan Van Cleynenbreugel ◽  
...  
Keyword(s):  
Low Dose ◽  
Colonic Polyps ◽  
Computer Aided Detection ◽  
Low Dose Ct ◽  
Computer Aided

scholarly journals Improved Curvature Estimation for Computer-aided Detection of Colonic Polyps in CT Colonography

2011 ◽  
Vol 18 (8) ◽  
pp. 1024-1034 ◽  
Author(s):  
Hongbin Zhu ◽  
Yi Fan ◽  
Hongbing Lu ◽  
Zhengrong Liang
Keyword(s):  
Ct Colonography ◽  
Colonic Polyps ◽  
Computer Aided Detection ◽  
Computer Aided ◽  
Curvature Estimation

scholarly journals Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection

2012 ◽  
Vol 03 (06) ◽  
pp. 1020-1028 ◽  
Author(s):  
Edén A. Alanís-Reyes ◽  
José L. Hernández-Cruz ◽  
Jesús S. Cepeda ◽  
Camila Castro ◽  
Hugo Terashima-Marín ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Computer Aided Detection ◽  
Learning Techniques ◽  
Computer Aided ◽  
Microcalcification Clusters

scholarly journals Machine Learning based sampling of X-Ray images for a computer-aided detection of Tuberculosis

2021 ◽  
Author(s):  
Fernando Ferreira ◽  
Philipp Gaspar ◽  
Lukas Müller Oliveira ◽  
Rodrigo Torres ◽  
Micael Veríssimo Araújo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Topological Analysis ◽  
Machine Learning Algorithms ◽  
Self Organizing Map ◽  
X Rays ◽  
Computer Aided Detection ◽  
Tuberculosis Screening ◽  
Data Set ◽  
Computer Aided ◽  
Screening Platform

Computer Aided Detection software relies on an annotated data set of X-rays to be developed. The annotation task requires extensive know-how and it is very time-consuming. This work presents a sampling method to select the most relevant images which will be annotated for the development of Tuberculosis screening platform based on machine learning algorithms. The sampling task optimizes the annotation process by reducing the number of images to be analyzed without compromising the diversity and the significance power of the images in the dataset. In this context, the image relevance is based on similarity and dissimilarity measurements. The experiment consisted in a deep learning feature engineering step, followed by topological analysis based on Self-Organizing Map and K-Means.

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