Darwin's Neural Network: AI-based Strategies for Rapid and Scalable Cell and Coronavirus Screening

• URL: http://arxiv.org/abs/2007.11653v1
• Date: Wed, 22 Jul 2020 20:11:06 GMT
• Title: Darwin's Neural Network: AI-based Strategies for Rapid and Scalable Cell and Coronavirus Screening
• Authors: Sang Won Lee, Yueh-Ting Chiu, Philip Brudnicki, Audrey M. Bischoff, Angus Jelinek, Jenny Zijun Wang, Danielle R. Bogdanowicz, Andrew F. Laine, Jia Guo, and Helen H. Lu
• Abstract summary: Machine learning algorithms are transforming the interpretation and analysis of microscope and nanoscope imaging data. These advances are enabling researchers to carry out real-time experiments that were previously thought to be computationally impossible. Here we adapt the theory of survival of the fittest in the field of computer vision and machine perception to introduce a new framework of multi-class instance segmentation deep learning.
• Score: 10.775030345262676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in the interdisciplinary scientific field of machine perception, computer vision, and biomedical engineering underpin a collection of machine learning algorithms with a remarkable ability to decipher the contents of microscope and nanoscope images. Machine learning algorithms are transforming the interpretation and analysis of microscope and nanoscope imaging data through use in conjunction with biological imaging modalities. These advances are enabling researchers to carry out real-time experiments that were previously thought to be computationally impossible. Here we adapt the theory of survival of the fittest in the field of computer vision and machine perception to introduce a new framework of multi-class instance segmentation deep learning, Darwin's Neural Network (DNN), to carry out morphometric analysis and classification of COVID19 and MERS-CoV collected in vivo and of multiple mammalian cell types in vitro.

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