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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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.