Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence
Bangfeng Wang,Yiwei Li,Mengfan Zhou,Yulong Han,Mingyu Zhang,Zhaolong Gao,Zetai Liu,Peng Chen,Wei Du,Jing Zhang,Xiao Jun Feng,Bi-Feng Liu +11 more
TL;DR: In this article , the authors summarize recent progress in mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms.
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Abstract: The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms.
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Pocket schlieren: a background-oriented schlieren imaging platform on a smartphone
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Abstract: Background-Oriented Schlieren (BOS) is a powerful technique for flow visualization. Nevertheless, the widespread dissemination of BOS is impeded by its dependence on scientific cameras, computing hardware, and dedicated analysis software. In this work, we aim to democratize BOS by providing a smartphone-based, open-access scientific tool called “Pocket Schlieren”. Pocket schlieren enables users to directly capture, process, and visualize flow phenomena on their smartphones. The underlying algorithm incorporates consecutive frame subtraction (CFS) and optical flow (OF) techniques to show the density gradients inside a flow. It performs on both engineered and natural background patterns. Using pocket schlieren, we successfully visualized the flow produced from a burning candle flame, butane lighter, hot soldering iron, room heater, water immersion heating rod, and a large outdoor butane flame. We have also demonstrated the flow visualization in liquid medium. It is able to detect minuscule refractive index variations up to the order of ~ 10–3. Pocket schlieren promises to serve as a frugal yet potent instrument for scientific and educational purposes.
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TL;DR: A conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each, which is easily extended to zero- shot learning.
•Posted Content
Learning to Compare: Relation Network for Few-Shot Learning
TL;DR: Relation Network (RN) as mentioned in this paper learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting.