A Deep Convolutional Neural Network Trained on Representative Samples for Circulating Tumor Cell Detection

Abstract

The number of Circulating Tumor Cells (CTCs) in blood indicates the tumor response to chemotherapeutic agents and disease progression. In early cancer diagnosis and treatment monitoring routine, detection and enumeration of CTCs in clinical blood samples have significant applications. In this paper, we design a Deep Convolutional Neural Network (DCNN) with automatically learned features for image-based CTC detection. We also present an effective training methodology which finds the most representative training samples to define the classification boundary between positive and negative samples. In the experiment, we compare the performance of auto-learned feature from DCNN and hand-crafted features, in which the DCNN outperforms hand-crafted feature. We also prove that the proposed training methodology is effective in improving the performance of DCNN classifiers.

Meeting Name

2016 IEEE Winter Conference on Applications of Computer Vision (2016: Mar. 7-10, Lake Placid, NY)

Department(s)

Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Blood; Cells; Computer Vision; Convolution; Neural Networks; Tumors; Chemotherapeutic Agents; Circulating Tumor Cells; Classification Boundary; Convolutional Neural Network; Detection and Enumerations; Disease Progression; Representative Sample; Treatment Monitoring; Diagnosis

International Standard Book Number (ISBN)

978-1509006410

International Standard Serial Number (ISSN)

2472-6737

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 May 2016

Link to Full Text

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