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

Author(s): Yunxiang Mao, Zhaozheng Yin and Joseph M. Schober
Date of Publication: March 2016
Abstract:

The number of Circulating Tumor Cells (CTCs) in blood indicates the tumor response to hemotherapeutic 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 features from DCNN and hand-crafted features, in which the DCNN outperforms hand-crafted features. We also prove that the proposed training methodology is effective in improving the performance of DCNN classifiers.

Citation: Mao, Y., Yin, Z., & Schober, J. (2016). A deep convolutional neural network trained on representative samples for circulating tumor cell detection. 2016 IEEE Winter Conference on Applications of Computer Vision.
Team(s): Plant Team