Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics
Abstract
Point of care diagnostics using microscopy and computer vision methods have been applied to a number of practical problems, and are particularly relevant to low-income, high disease burden areas. However, this is subject to the limitations in sensitivity and specificity of the computer vision methods used. In general, deep learning has recently revolutionised the field of computer vision, in some cases surpassing human performance for other object recognition tasks. In this paper, we evaluate the performance of deep convolutional neural networks on three different microscopy tasks: diagnosis of malaria in thick blood smears, tuberculosis in sputum samples, and intestinal parasite eggs in stool samples. In all cases accuracy is very high and substantially better than an alternative approach more representative of traditional medical imaging techniques.
Cite
Text
Quinn et al. "Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics." Proceedings of the 1st Machine Learning for Healthcare Conference, 2016.Markdown
[Quinn et al. "Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics." Proceedings of the 1st Machine Learning for Healthcare Conference, 2016.](https://mlanthology.org/mlhc/2016/quinn2016mlhc-deep/)BibTeX
@inproceedings{quinn2016mlhc-deep,
title = {{Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics}},
author = {Quinn, John A and Nakasi, Rose and Mugagga, Pius K. B. and Byanyima, Patrick and Lubega, William and Andama, Alfred},
booktitle = {Proceedings of the 1st Machine Learning for Healthcare Conference},
year = {2016},
pages = {271-281},
volume = {56},
url = {https://mlanthology.org/mlhc/2016/quinn2016mlhc-deep/}
}