Complex field recovery from on-axis digital hologram using deep learning

2021 ◽  
Author(s):  
Yeon-Gyeong Ju ◽  
Jae-Hyeung Park
Keyword(s):  
Deep Learning ◽  
Complex Field ◽  
Digital Hologram

Nonblind Image Deblurring via Deep Learning in Complex Field

2021 ◽  
pp. 1-14
Author(s):  
Yuhui Quan ◽  
Peikang Lin ◽  
Yong Xu ◽  
Yuesong Nan ◽  
Hui Ji
Keyword(s):  
Deep Learning ◽  
Image Deblurring ◽  
Complex Field

A Deep Learning Approach for Digital Hologram Speckle Noise Reduction

2020 ◽  
Author(s):  
Wen-Jing Zhou ◽  
Shili Liu ◽  
Hongbo Zhang ◽  
Yingjie Yu ◽  
Ting-Chung Poon
Keyword(s):  
Deep Learning ◽  
Noise Reduction ◽  
Speckle Noise ◽  
Learning Approach ◽  
Digital Hologram ◽  
Speckle Noise Reduction

scholarly journals Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 33679-33688 ◽  
Author(s):  
Jun Sun ◽  
Yu Yang ◽  
Xiaofei He ◽  
Xiaohong Wu
Keyword(s):  
Deep Learning ◽  
Leaf Blight ◽  
Complex Field ◽  
Maize Leaf ◽  
Field Environment

Deep Learning

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

Identifying Patterns in Complex Field Data

2017 ◽  
Vol 225 (3) ◽  
pp. 268-284 ◽  
Author(s):  
Andrew J. White ◽  
Dieter Kleinböhl ◽  
Thomas Lang ◽  
Alfons O. Hamm ◽  
Alexander L. Gerlach ◽  
...  
Keyword(s):  
Heart Rate ◽  
Panic Disorder ◽  
Latent Class ◽  
Treatment Protocol ◽  
Situational Factors ◽  
Self Report ◽  
Complex Field ◽  
Cluster Assignment ◽  
Experimental Conditions ◽  
Conventional Analysis

Abstract. Ambulatory assessment methods are well suited to examine how patients with panic disorder and agoraphobia (PD/A) undertake situational exposure. But under complex field conditions of a complex treatment protocol, the variability of data can be so high that conventional analytic approaches based on group averages inadequately describe individual variability. To understand how fear responses change throughout exposure, we aimed to demonstrate the incremental value of sorting HR responses (an index of fear) prior to applying averaging procedures. As part of their panic treatment, 85 patients with PD/A completed a total of 233 bus exposure exercises. Heart rate (HR), global positioning system (GPS) location, and self-report data were collected. Patients were randomized to one of two active treatment conditions (standard exposure or fear-augmented exposure) and completed multiple exposures in four consecutive exposure sessions. We used latent class cluster analysis (CA) to cluster heart rate (HR) responses collected at the start of bus exposure exercises (5 min long, centered on bus boarding). Intra-individual patterns of assignment across exposure repetitions were examined to explore the relative influence of individual and situational factors on HR responses. The association between response types and panic disorder symptoms was determined by examining how clusters were related to self-reported anxiety, concordance between HR and self-report measures, and bodily symptom tolerance. These analyses were contrasted with a conventional analysis based on averages across experimental conditions. HR responses were sorted according to form and level criteria and yielded nine clusters, seven of which were interpretable. Cluster assignment was not stable across sessions or treatment condition. Clusters characterized by a low absolute HR level that slowly decayed corresponded with low self-reported anxiety and greater self-rated tolerance of bodily symptoms. Inconsistent individual factors influenced HR responses less than situational factors. Applying clustering can help to extend the conventional analysis of highly variable data collected in the field. We discuss the merits of this approach and reasons for the non-stereotypical pattern of cluster assignment across exposures.

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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