Keyword search (4,163 papers available)

"neural network" Keyword-tagged Publications:

Title Authors PubMed ID
1 Tuning Deep Learning for Predicting Aluminum Prices Under Different Sampling: Bayesian Optimization Versus Random Search Alicia Estefania Antonio Figueroa 41751647
CONCORDIA
2 Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm Lahmiri S; Tadj C; Gargour C; 41294952
ENCS
3 Efficient neural encoding as revealed by bilingualism Moore C; Donhauser PW; Klein D; Byers-Heinlein K; 40828024
PSYCHOLOGY
4 Personalizing brain stimulation: continual learning for sleep spindle detection Sobral M; Jourde HR; Marjani Bajestani SE; Coffey EBJ; Beltrame G; 40609549
PSYCHOLOGY
5 PARPAL: PARalog Protein Redistribution using Abundance and Localization in Yeast Database Greco BM; Zapata G; Dandage R; Papkov M; Pereira V; Lefebvre F; Bourque G; Parts L; Kuzmin E; 40580499
BIOLOGY
6 Distributed adaptive sliding mode control with deep recurrent neural network for cooperative robotic system in automated fiber placement Zhu N; Xie WF; 40436653
ENCS
7 Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting Ahmed U; Mahmood A; Khan AR; Kuhlmann L; Alimgeer KS; Razzaq S; Aziz I; Hammad A; 40185800
PHYSICS
8 Large language models deconstruct the clinical intuition behind diagnosing autism Stanley J; Rabot E; Reddy S; Belilovsky E; Mottron L; Bzdok D; 40147442
ENCS
9 CACTUS: An open dataset and framework for automated Cardiac Assessment and Classification of Ultrasound images using deep transfer learning Elmekki H; Alagha A; Sami H; Spilkin A; Zanuttini AM; Zakeri E; Bentahar J; Kadem L; Xie WF; Pibarot P; Mizouni R; Otrok H; Singh S; Mourad A; 40107020
ENCS
10 MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle McKay MJ; Weber KA; Wesselink EO; Smith ZA; Abbott R; Anderson DB; Ashton-James CE; Atyeo J; Beach AJ; Burns J; Clarke S; Collins NJ; Coppieters MW; Cornwall J; Crawford RJ; De Martino E; Dunn AG; Eyles JP; Feng HJ; Fortin M; Franettovich Smith MM; Galloway G; Gandomkar Z; Glastras S; Henderson LA; Hides JA; Hiller CE; Hilmer SN; Hoggarth MA; Kim B; Lal N; LaPorta L; Magnussen JS; Maloney S; March L; Nackley AG; O' Leary SP; Peolsson A; Perraton Z; Pool-Goudzwaard AL; Schnitzler M; Seitz AL; Semciw AI; Sheard PW; Smith AC; Snodgrass SJ; Sullivan J; Tran V; Valentin S; Walton DM; Wishart LR; Elliott JM; 39590726
HKAP
11 Ion channel classification through machine learning and protein language model embeddings Ghazikhani H; Butler G; 39572876
ENCS
12 A protocol for trustworthy EEG decoding with neural networks Borra D; Magosso E; Ravanelli M; 39549492
ENCS
13 Position-based visual servoing of a 6-RSS parallel robot using adaptive sliding mode control Zhu N; Xie WF; Shen H; 39492316
ENCS
14 Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks Adcock B; Brugiapaglia S; Dexter N; Moraga S; 39454372
MATHSTATS
15 Deep neural network-based robotic visual servoing for satellite target tracking Ghiasvand S; Xie WF; Mohebbi A; 39440297
ENCS
16 Generalization limits of Graph Neural Networks in identity effects learning D' Inverno GA; Brugiapaglia S; Ravanelli M; 39426036
ENCS
17 Modelling reindeer rut activity using on-animal acoustic recorders and machine learning Boucher AJ; Weladji RB; Holand Ø; Kumpula J; 38932958
BIOLOGY
18 The immunomodulatory effect of oral NaHCO3 is mediated by the splenic nerve: multivariate impact revealed by artificial neural networks Alvarez MR; Alkaissi H; Rieger AM; Esber GR; Acosta ME; Stephenson SI; Maurice AV; Valencia LMR; Roman CA; Alarcon JM; 38549144
CSBN
19 Enhanced identification of membrane transport proteins: a hybrid approach combining ProtBERT-BFD and convolutional neural networks Ghazikhani H; Butler G; 37497772
ENCS
20 Compatible-domain Transfer Learning for Breast Cancer Classification with Limited Annotated Data Shamshiri MA; Krzyzak A; Kowal M; Korbicz J; 36758326
ENCS
21 Neural correlates of recall and extinction in a rat model of appetitive Pavlovian conditioning Brown A; Villaruel FR; Chaudhri N; 36496079
PSYCHOLOGY
22 Reinforcement learning for automatic quadrilateral mesh generation: A soft actor-critic approach Pan J; Huang J; Cheng G; Zeng Y; 36375347
ENCS
23 Sentiment Classification Method Based on Blending of Emoticons and Short Texts Zou H; Xiang K; 35327909
ENCS
24 Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation Vu HL; Ng KTW; Richter A; An C; 35287077
ENCS
25 Comparative Evaluation of Artificial Neural Networks and Data Analysis in Predicting Liposome Size in a Periodic Disturbance Micromixer Ocampo I; López RR; Camacho-León S; Nerguizian V; Stiharu I; 34683215
ENCS
26 Corrigendum: Deep Learning-Based Haptic Guidance for Surgical Skills Transfer Fekri P; Dargahi J; Zadeh M; 34026860
ENCS
27 X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech Jeancolas L; Petrovska-Delacrétaz D; Mangone G; Benkelfat BE; Corvol JC; Vidailhet M; Lehéricy S; Benali H; 33679361
PERFORM
28 Deep Learning-Based Haptic Guidance for Surgical Skills Transfer. Fekri P, Dargahi J, Zadeh M 33553246
ENCS

 

Title:MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle
Authors:McKay MJWeber KAWesselink EOSmith ZAAbbott RAnderson DBAshton-James CEAtyeo JBeach AJBurns JClarke SCollins NJCoppieters MWCornwall JCrawford RJDe Martino EDunn AGEyles JPFeng HJFortin MFranettovich Smith MMGalloway GGandomkar ZGlastras SHenderson LAHides JAHiller CEHilmer SNHoggarth MAKim BLal NLaPorta LMagnussen JSMaloney SMarch LNackley AGO'Leary SPPeolsson APerraton ZPool-Goudzwaard ALSchnitzler MSeitz ALSemciw AISheard PWSmith ACSnodgrass SJSullivan JTran VValentin SWalton DMWishart LRElliott JM
Link:https://pubmed.ncbi.nlm.nih.gov/39590726/
DOI:10.3390/jimaging10110262
Publication:Journal of imaging
Keywords:MR imagingartificial intelligencemachine learningmuscle fat infiltrationneural networksnormative reference datapublic datasets
PMID:39590726 Category: Date Added:2024-11-26
Dept Affiliation: HKAP
1 Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia.
2 Division of Pain Medicine, Stanford University School of Medicine, Stanford University, Stanford, CA 94304, USA.
3 Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences-Program Musculoskeletal Health, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands.
4 Department of Rehabilitation Medicine, University of Oklahoma, Norman, OK 73019, USA.
5 Department of Rehabilitation Medicine, University of Minnesota, Minneapolis, MN 55455, USA.
6 Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia.
7 Disability Prevention Program, Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
8 School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, 4072 QLD, Australia.
9 School of Health Sciences and Social Work, Griffith University, Brisbane, QLD 4111, Australia.
10 Otago Medical School, University of Otago, Dunedin 9016, New Zealand.
11 Faculty of Health Sciences, Curtin University, Perth, WA 6845, Australia.
12 Department of Health Science and Technology, Aalborg University, Gistrup, 9260 North Jutland, Denmark.
13 Northern Sydney Local Health District, The Kolling Institute, St Leonards, NSW 2065, Australia.
14 Department of Health, Kinesiology & Applied Physiology, Concordia University, Montreal, QC H4B 1R6, Canada.
15 Herston Imaging Research Facility, University of Queensland, Brisbane, QLD 4072, Australia.
16 Department of Physical Therapy, North Central College, Naperville, IL 60540, USA.
17 School of Rehabilitative and Health Sciences, Regis University, Denver, CO 80221, USA.
18 Center for Translational Pain Medicine, Department of Anesthesiology, School of Medicine, Duke University, Durham, NC 27710, USA.
19 Occupational and Environmental Medicine Centre, Department of Health Medicine and Caring Sciences, Unit of Clinical Medicine, Linköping University, 58183 Linköping, Sweden.
20 Department of Health Medicine and Caring Sciences, Unit of Physiotherapy, Linköping University, 58183 Linköping, Sweden.
21 School of Allied Health, La Trobe University, Melbourne, VIC 3086, Australia.
22 Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
23 School of Medicine, University of Colorado, Aurora, CO 80045, USA.
24 Discipline of Physiotherapy, University of Newcastle, Callaghan, NSW 2308, Australia.
25 Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia.
26 School of Health & Social Care, Edinburgh Napier University, Edinburgh, Scotland EH11 4BN, UK.
27 School of Physical Therapy, Western University, London, ON N6A 3K7, Canada.
28 School of Medicine and Dentistry, Griffith University, Brisbane, QLD 4111, Australia.

Description:

Disorders affecting the neurological and musculoskeletal systems represent international health priorities. A significant impediment to progress in trials of new therapies is the absence of responsive, objective, and valid outcome measures sensitive to early disease changes. A key finding in individuals with neuromuscular and musculoskeletal disorders is the compositional changes to muscles, evinced by the expression of fatty infiltrates. Quantification of skeletal muscle composition by MRI has emerged as a sensitive marker for the severity of these disorders; however, little is known about the composition of healthy muscles across the lifespan. Knowledge of what is 'typical' age-related muscle composition is essential to accurately identify and evaluate what is 'atypical'. This innovative project, known as the MuscleMap, will achieve the first important steps towards establishing a world-first, normative reference MRI dataset of skeletal muscle composition with the potential to provide valuable insights into various diseases and disorders, ultimately improving patient care and advancing research in the field.





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