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.
Cite
Text
Mao et al. "A Deep Convolutional Neural Network Trained on Representative Samples for Circulating Tumor Cell Detection." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016. doi:10.1109/WACV.2016.7477603Markdown
[Mao et al. "A Deep Convolutional Neural Network Trained on Representative Samples for Circulating Tumor Cell Detection." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016.](https://mlanthology.org/wacv/2016/mao2016wacv-deep/) doi:10.1109/WACV.2016.7477603BibTeX
@inproceedings{mao2016wacv-deep,
title = {{A Deep Convolutional Neural Network Trained on Representative Samples for Circulating Tumor Cell Detection}},
author = {Mao, Yunxiang and Yin, Zhaozheng and Schober, Joseph M.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2016},
pages = {1-6},
doi = {10.1109/WACV.2016.7477603},
url = {https://mlanthology.org/wacv/2016/mao2016wacv-deep/}
}