RT Journal Article SR Electronic T1 Holographic deep learning for rapid optical screening of anthrax spores JF bioRxiv FD Cold Spring Harbor Laboratory SP 109108 DO 10.1101/109108 A1 YoungJu Jo A1 Sangjin Park A1 JaeHwang Jung A1 Jonghee Yoon A1 Hosung Joo A1 Min-hyeok Kim A1 Suk-Jo Kang A1 Myung Chul Choi A1 Sang Yup Lee A1 YongKeun Park YR 2017 UL http://biorxiv.org/content/early/2017/02/16/109108.abstract AB 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.