Automation of leaf counting in maize and sorghum using deep learning

Chenyong Miao; Alice Guo; Addie M. Thompson; Jinliang Yang; Yufeng Ge; James C. Schnable

doi:10.1002/ppj2.20022

Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

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.

EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE USING DEEP LEARNING FOR DETECTING GASTRIC CANCER IN ENDOSCOPIC IMAGES

10.1055/s-0038-1637183 ◽

2018 ◽

Author(s):

T Hirasawa ◽

K Aoyama ◽

J Fujisaki ◽

T Tada

Keyword(s):

Artificial Intelligence ◽

Gastric Cancer ◽

Deep Learning ◽

Endoscopic Images

Vollautomatische Quantifizierung der Volumina und Funktion des linken Ventrikels in der kardialen MRT: Evaluation eines Deep-Learning basierten Algorithmus

10.1055/s-0040-1703162 ◽

2020 ◽

Author(s):

B Böttcher ◽

E Beller ◽

A Busse ◽

F Streckenbach ◽

M Weber ◽

...

Keyword(s):

Deep Learning

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

10.1055/s-0040-1703317 ◽

2020 ◽

Author(s):

L Pennig ◽

L Lourenco Caldeira ◽

C Hoyer ◽

L Görtz ◽

R Shahzad ◽

...

Keyword(s):

Deep Learning

Computertomografie: Bildrekonstruktion mit Deep Learning in der klinischen Routine

10.1055/s-0040-1703110 ◽

2020 ◽

Author(s):

A Heinrich ◽

M Engler ◽

D Dachoua ◽

U Teichgräber ◽

F Güttler

Keyword(s):

Deep Learning

A DEEP LEARNING-BASED SYSTEM FOR IDENTIFYING DIFFERENTIATION STATUS AND DELINEATING MARGINS OF EARLY GASTRIC CANCER IN NARROW-BAND IMAGING ENDOSCOPY

10.1055/s-0040-1704291 ◽

2020 ◽

Author(s):

H Yu ◽

L Wu ◽

T Ling ◽

S Hu

Keyword(s):

Gastric Cancer ◽

Deep Learning ◽

Early Gastric Cancer ◽

Narrow Band ◽

Narrow Band Imaging ◽

Differentiation Status

AUTOMATED POLYP SIZE ESTIMATION WITH DEEP LEARNING REDUCES OVERESTIMATION BIAS

10.1055/s-0040-1704526 ◽

2020 ◽

Author(s):

J Suykens ◽

T Eelbode ◽

J Daenen ◽

P Suetens ◽

F Maes ◽

...

Keyword(s):

Deep Learning ◽

Size Estimation ◽

Polyp Size

COMPARISON OF THE PRECISION OF OPTICAL DIFFERENTATION BETWEEN NEOPLASTIC AND NON-NEOPLASTIC SUBCENTRIMETRIC POLYP HISTOLOGY BY ENDOSCOPIC EXPERTS AND ARTEFICIAL INTELLIGENCE DEEP LEARNING NEURAL NETWORK (POLYPBRAIN)

10.1055/s-0040-1704154 ◽

2020 ◽

Author(s):

L Madácsy ◽

M Szalai ◽

Á Finta ◽

K Zsobrák ◽

L Oczella ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Learning Neural Network

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189048 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5699-5711

Author(s):

Shirong Long ◽

Xuekong Zhao

Keyword(s):

Feature Extraction ◽

Particle Swarm Optimization ◽

Deep Learning ◽

Real Time ◽

Image Recognition ◽

Particle Swarm ◽

Learning Technology ◽

Search Performance ◽

Swarm Optimization ◽

Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.