Iteratively Training Classifiers for Circulating Tumor Cell Detection

Abstract

The number of Circulating Tumor Cells (CTCs) in blood provides an indication of disease progression and tumor response to chemotherapeutic agents. Hence, routine detection and enumeration of CTCs in clinical blood samples have significant applications in early cancer diagnosis and treatment monitoring. In this paper, we investigate two classifiers for image-based CTC detection: (1) Support Vector Machine (SVM) with hard-coded Histograms of Oriented Gradients (HoG) features; and (2) Convolutional Neural Network (CNN) with automatically learned features. For both classifiers, we present an effective and efficient training algorithm, by which the most representative negative samples are iteratively collected to accurately define the classification boundary between positive and negative samples. The two iteratively trained classifiers are validated on a challenging dataset with high performance.

Meeting Name

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) (2015: Apr. 16-19 Brooklyn, NYA)

Department(s)

Computer Science

Keywords and Phrases

Blood; Cells; Classification (of Information); Convolution; Diagnosis; Iterative Methods; Medical Imaging; Neural Networks; Support Vector Machines; Tumors; Chemotherapeutic Agents; Circulating Tumor Cells; Classification Boundary; Convolutional Neural Network; Disease Progression; Histograms of Oriented Gradients (Hog); Training Algorithms; Treatment Monitoring; Image Classification; Iterative Training; Support Vector Machine

International Standard Book Number (ISBN)

978-1479923748

International Standard Serial Number (ISSN)

1945-7928

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jul 2015

Link to Full Text

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