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Emergent physics-informed design of deep learning for microscopy
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dc.contributor.author | Wijesinghe, Philip | |
dc.contributor.author | Dholakia, Kishan | |
dc.date.accessioned | 2021-05-06T16:30:19Z | |
dc.date.available | 2021-05-06T16:30:19Z | |
dc.date.issued | 2021-04 | |
dc.identifier | 273444909 | |
dc.identifier | 29e0c603-9d9a-4f76-9c8f-87e80e8941db | |
dc.identifier | 000640393100001 | |
dc.identifier | 85104849464 | |
dc.identifier.citation | Wijesinghe , P & Dholakia , K 2021 , ' Emergent physics-informed design of deep learning for microscopy ' , Journal of Physics: Photonics , vol. 3 , no. 2 , 021003 . https://doi.org/10.1088/2515-7647/abf02c | en |
dc.identifier.issn | 2515-7647 | |
dc.identifier.uri | https://hdl.handle.net/10023/23127 | |
dc.description | Funding: UK Engineering and Physical Sciences Research Council through grant EP/P030017/1. | en |
dc.description.abstract | Deep learning has revolutionised microscopy, enabling automated means for image classification, tracking and transformation. Going beyond machine vision, deep learning has recently emerged as a universal and powerful tool to address challenging and previously untractable image recovery problems. In seeking accurate, learned means of inversion, these advances have transformed conventional deep learning methods to those cognisant of the underlying physics of image formation, enabling robust, efficient and accurate recovery even in severely ill-posed conditions. In this Perspective, we explore the emergence of physics-informed deep learning that will enable universal and accessible computational microscopy. | |
dc.format.extent | 11 | |
dc.format.extent | 1193981 | |
dc.language.iso | eng | |
dc.relation.ispartof | Journal of Physics: Photonics | en |
dc.subject | Deep learning | en |
dc.subject | Microscopy | en |
dc.subject | Inverse methods | en |
dc.subject | Physics-informed learning | en |
dc.subject | Computational imaging | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | QC Physics | en |
dc.subject.lcc | QA75 | en |
dc.subject.lcc | QC | en |
dc.title | Emergent physics-informed design of deep learning for microscopy | en |
dc.type | Journal item | en |
dc.contributor.sponsor | EPSRC | en |
dc.contributor.institution | University of St Andrews. School of Physics and Astronomy | en |
dc.contributor.institution | University of St Andrews. Sir James Mackenzie Institute for Early Diagnosis | en |
dc.contributor.institution | University of St Andrews. Centre for Biophotonics | en |
dc.contributor.institution | University of St Andrews. Biomedical Sciences Research Complex | en |
dc.identifier.doi | https://doi.org/10.1088/2515-7647/abf02c | |
dc.description.status | Peer reviewed | en |
dc.identifier.grantnumber | EP/P030017/1 | en |
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