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Holographic deep learning for rapid optical screening of anthrax spores

YoungJu Jo, Sangjin Park, JaeHwang Jung, Jonghee Yoon, Hosung Joo, Min-hyeok Kim, Suk-Jo Kang, Myung Chul Choi, Sang Yup Lee, YongKeun Park
doi: https://doi.org/10.1101/109108
YoungJu Jo
a Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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Sangjin Park
b Department of Chemical and Biomolecular Engineering, KAIST, Daejeon 34141, Republic of Korea
c Agency for Defense Development (ADD), Daejeon 34186, Republic of Korea.
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JaeHwang Jung
a Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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Jonghee Yoon
a Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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Hosung Joo
d School of Electrical Engineering, KAIST, Daejeon 34141, Republic of Korea
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Min-hyeok Kim
e Department of Biological Sciences, KAIST, Daejeon 34141, Republic of Korea
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Suk-Jo Kang
e Department of Biological Sciences, KAIST, Daejeon 34141, Republic of Korea
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Myung Chul Choi
f Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea
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Sang Yup Lee
b Department of Chemical and Biomolecular Engineering, KAIST, Daejeon 34141, Republic of Korea
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  • For correspondence: leesy@kaist.ac.kr yk.park@kaist.ac.kr
YongKeun Park
a Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
g Tomocube Inc., Daejeon 34051, Republic of Korea
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  • For correspondence: leesy@kaist.ac.kr yk.park@kaist.ac.kr
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Abstract

Establishing early warning systems for anthrax attacks is crucial in biodefense. Here we present an optical method for rapid screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells. After training, the network outperforms previous techniques in all accuracy measures, achieving single-spore sensitivity and sub-genus specificity. The unique ‘representation learning’ capability of deep learning enables direct training from raw images instead of manually extracted features. The method automatically recognizes key biological traits encoded in the images and exploits them as fingerprints. This remarkable learning ability makes the proposed method readily applicable to classifying various single cells in addition to B. anthracis, as demonstrated for the diagnosis of Listeria monocytogenes, without any modification. We believe that our strategy will make holographic microscopy more accessible to medical doctors and biomedical scientists for easy, rapid, and accurate diagnosis of pathogens, and facilitate exciting new applications.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 16, 2017.
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Holographic deep learning for rapid optical screening of anthrax spores
YoungJu Jo, Sangjin Park, JaeHwang Jung, Jonghee Yoon, Hosung Joo, Min-hyeok Kim, Suk-Jo Kang, Myung Chul Choi, Sang Yup Lee, YongKeun Park
bioRxiv 109108; doi: https://doi.org/10.1101/109108
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Holographic deep learning for rapid optical screening of anthrax spores
YoungJu Jo, Sangjin Park, JaeHwang Jung, Jonghee Yoon, Hosung Joo, Min-hyeok Kim, Suk-Jo Kang, Myung Chul Choi, Sang Yup Lee, YongKeun Park
bioRxiv 109108; doi: https://doi.org/10.1101/109108

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