Deep learning allows enhanced detection of surface plasmon scattering (Conference Presentation)

2020 ◽  
Author(s):  
Gwiyeong Moon ◽  
Taehwang Son ◽  
Hongki Lee ◽  
Donghyun Kim
Keyword(s):  
Deep Learning ◽  
Surface Plasmon

Deep Learning Approach for Enhanced Detection of Surface Plasmon Scattering

2019 ◽  
Vol 91 (15) ◽  
pp. 9538-9545 ◽  
Author(s):  
Gwiyeong Moon ◽  
Taehwang Son ◽  
Hongki Lee ◽  
Donghyun Kim
Keyword(s):  
Deep Learning ◽  
Surface Plasmon ◽  
Learning Approach

Improved measurement of surface plasmon scattering based on deep learning

2020 ◽  
Author(s):  
Gwiyeong Moon ◽  
Taehwang Son ◽  
Hongki Lee ◽  
Donghyun Kim
Keyword(s):  
Deep Learning ◽  
Surface Plasmon

scholarly journals Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kitsada Thadson ◽  
Sarinporn Visitsattapongse ◽  
Suejit Pechprasarn
Keyword(s):  
Refractive Index ◽  
Deep Learning ◽  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
Phase Retrieval ◽  
Learning Algorithm ◽  
Single Shot ◽  
Refractive Index Sensing ◽  
Deep Learning Algorithm

AbstractA deep learning algorithm for single-shot phase retrieval under a conventional microscope is proposed and investigated. The algorithm has been developed using the context aggregation network architecture; it requires a single input grayscale image to predict an output phase profile through deep learning-based pattern recognition. Surface plasmon resonance imaging has been employed as an example to demonstrate the capability of the deep learning-based method. The phase profiles of the surface plasmon resonance phenomena have been very well established and cover ranges of phase transitions from 0 to 2π rad. We demonstrate that deep learning can be developed and trained using simulated data. Experimental validation and a theoretical framework to characterize and quantify the performance of the deep learning-based phase retrieval method are reported. The proposed deep learning-based phase retrieval performance was verified through the shot noise model and Monte Carlo simulations. Refractive index sensing performance comparing the proposed deep learning algorithm and conventional surface plasmon resonance measurements are also discussed. Although the proposed phase retrieval-based algorithm cannot achieve a typical detection limit of 10–7 to 10–8 RIU for phase measurement in surface plasmon interferometer, the proposed artificial-intelligence-based approach can provide at least three times lower detection limit of 4.67 × 10–6 RIU compared to conventional intensity measurement methods of 1.73 × 10–5 RIU for the optical energy of 2500 pJ with no need for sophisticated optical interferometer instrumentation.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Primäre ZNS-Lymphome: Vollautomatisierte Detektion und Segmentierung auf multiparametrischen MRT-Datensätzen mittels Deep-Learning

2020 ◽  
Author(s):  
L Pennig ◽  
L Lourenco Caldeira ◽  
C Hoyer ◽  
L Görtz ◽  
R Shahzad ◽  
...  
Keyword(s):  
Deep Learning
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