A Deep Convolutional Neural Network Trained on Representative Samples for Circulating Tumor Cell Detection
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.
Y. Mao et al., "A Deep Convolutional Neural Network Trained on Representative Samples for Circulating Tumor Cell Detection," Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016 (2016, Lake Placid, NY), pp. 1-6, Institute of Electrical and Electronics Engineers (IEEE), May 2016.
The definitive version is available at http://dx.doi.org/10.1109/WACV.2016.7477603
2016 IEEE Winter Conference on Applications of Computer Vision (2016: Mar. 7-10, Lake Placid, NY)
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
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Article - Conference proceedings
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