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1Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network
A. Iporre-Rivas, D. Saur, K. Rohr, G. Scheuermann, and C. Gillmann. J. Med. Imaging (Bellingham), 10 (4): 044502 (July 2023)
3 months ago by @scadsfct
show all tags
topic_visualcomputing
graph
imaging;
learning;
machine
medical
multi-modal
networks;
neural
prediction
stroke
topic_visualcomputinggraphimaging;learning;machinemedicalmulti-modalnetworks;neuralpredictionstroke
(0)
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1Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach
M. Steinacker, Y. Kheifetz, and M. Scholz. Heliyon, 9 (7): e17890 (July 2023)
3 months ago by @scadsfct
show all tags
topic_neuroinspired
topic_mathfoundation
Haematopoiesis;
Precision
Recurrent
System
Transfer
identification;
learning
medicine;
networks;
neural
unit_transfer
topic_neuroinspiredtopic_mathfoundationHaematopoiesis;PrecisionRecurrentSystemTransferidentification;learningmedicine;networks;neuralunit_transfer
(0)
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1Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review
L. Schneider, S. Laiouar-Pedari, S. Kuntz, E. Krieghoff-Henning, A. Hekler, J. Kather, T. Gaiser, S. Fröhling, and T. Brinker. Eur. J. Cancer, (January 2022)
3 months ago by @scadsfct
show all tags
topic_lifescience
Biomarker
Cancer;
Convolutional
Multimodal
Omics
fusion;
identification;
networks;
neural
topic_lifescienceBiomarkerCancer;ConvolutionalMultimodalOmicsfusion;identification;networks;neural
(0)
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1Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology
N. Ghaffari Laleh, H. Muti, C. Loeffler, A. Echle, O. Saldanha, F. Mahmood, M. Lu, C. Trautwein, R. Langer, B. Dislich and 13 other author(s). Med. Image Anal., 79 (102474): 102474 (July 2022)
3 months ago by @scadsfct
show all tags
topic_lifescience
Artificial
Computational
Convolutional
Learning;
Multiple-Instance
Vision
Weakly-supervised
deep
intelligence;
learning
networks;
neural
pathology;
transformers;
topic_lifescienceArtificialComputationalConvolutionalLearning;Multiple-InstanceVisionWeakly-superviseddeepintelligence;learningnetworks;neuralpathology;transformers;
(0)
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1Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction
D. Nam, J. Chapiro, V. Paradis, T. Seraphin, and J. Kather. JHEP Rep., 4 (4): 100443 (April 2022)
3 months ago by @scadsfct
show all tags
topic_lifescience
AI,
Artificial
CNN,
Communications
DICOM,
Diagnosis;
Digital
HCC,
Imaging
Individual
ML,
MVI,
Medicine;
NAFLD,
NASH,
Prognosis
Reporting
TACE,
TRIPOD,
Transparent
WSIs,
a
and
artificial
carcinoma;
chemoembolisation;
convolutional
data
deep
diagnostic
disease;
fatty
for
hepatocellular
images;
imaging;
in
integration
intelligence;
invasion;
learning;
liver
machine
microvascular
model
multimodal
multivariable
network;
neural
non-alcoholic
of
or
prediction
slide
steatohepatitis;
support
system;
transarterial
whole
topic_lifescienceAI,ArtificialCNN,CommunicationsDICOM,Diagnosis;DigitalHCC,ImagingIndividualML,MVI,Medicine;NAFLD,NASH,PrognosisReportingTACE,TRIPOD,TransparentWSIs,aandartificialcarcinoma;chemoembolisation;convolutionaldatadeepdiagnosticdisease;fattyforhepatocellularimages;imaging;inintegrationintelligence;invasion;learning;livermachinemicrovascularmodelmultimodalmultivariablenetwork;neuralnon-alcoholicoforpredictionslidesteatohepatitis;supportsystem;transarterialwhole
(0)
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1Introduction to the BioChemical Library (BCL): An application-based open-source toolkit for integrated cheminformatics and machine learning in computer-aided drug discovery
B. Brown, O. Vu, A. Geanes, S. Kothiwale, M. Butkiewicz, E. Lowe, Jr, R. Mueller, R. Pape, J. Mendenhall, and J. Meiler. Front. Pharmacol., (February 2022)
3 months ago by @scadsfct
show all tags
topic_lifescience
BCL;
QSAR;
biochemical
cheminformatics;
deep
design;
discovery;
drug
library;
network;
neural
open-source
topic_lifescienceBCL;QSAR;biochemicalcheminformatics;deepdesign;discovery;druglibrary;network;neuralopen-source
(0)
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1Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks
V. Scholz, P. Winkler, A. Hornig, M. Gude, and A. Filippatos. Sensors (Basel), 21 (6): 2005 (March 2021)
3 months ago by @scadsfct
show all tags
(SHM)
composite
composites;
connected
convolutional
dense
fully
health
learning;
machine
monitoring
networks;
neural
rotors;
structural
(SHM)compositecomposites;connectedconvolutionaldensefullyhealthlearning;machinemonitoringnetworks;neuralrotors;structural
(0)
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1TraceVis: Towards Visualization for Deep Statistical Model Checking
T. Gros, D. Groß, S. Gumhold, J. Hoffmann, M. Klauck, and M. Steinmetz. Leveraging Applications of Formal Methods, Verification and Validation: Tools and Trends: 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, Rhodes, Greece, October 20–30, 2020, Proceedings, Part IV, page 27–46. Berlin, Heidelberg, Springer-Verlag, (2020)
a month ago by @scadsfct
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Checking
Model
Networks
Neural
Statistical
Visualization
CheckingModelNetworksNeuralStatisticalVisualization
(0)
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1Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks
N. Kumar, L. Krause, T. Wondrak, S. Eckert, K. Eckert, and S. Gumhold. Sensors, (2024)
a month ago by @scadsfct
show all tags
topic_visualcomputing
Density
Flux
Fraction
Invertible
Magnetic
Networks
Neural
Noisy
Reconstruction
Void
topic_visualcomputingDensityFluxFractionInvertibleMagneticNetworksNeuralNoisyReconstructionVoid
(0)
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1A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP
J. Gerritzen, A. Hornig, P. Winkler, and M. Gude. Computational Materials Science, (September 2024)Publisher Copyright: © 2024 The Author(s).
19 days ago by @scadsfct
show all tags
area_architectures
topic_engineering
Constitutive
FIS_scads
Fiber
Machine
Neural
Parameter
identification
learning,
modeling,
networks,
plastics,
reinforced
area_architecturestopic_engineeringConstitutiveFIS_scadsFiberMachineNeuralParameteridentificationlearning,modeling,networks,plastics,reinforced
(0)
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1Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning
J. Gerritzen, A. Hornig, P. Winkler, and M. Gude. ECCM21 - Proceedings of the 21st European Conference on Composite Materials, 3, page 1252--1259. European Society for Composite Materials (ESCM), (Jul 2, 2024)21st European Conference on Composite Materials, ECCM 21 ; Conference date: 02-07-2024 Through 05-07-2024.
19 days ago by @scadsfct
show all tags
area_architectures
topic_engineering
Convolutional
Direct
FIS_scads
Fiber
Machine
Strain
dependency,
identification,
learning,
networks,
neural
parameter
plastics
rate
reinforced
area_architecturestopic_engineeringConvolutionalDirectFIS_scadsFiberMachineStraindependency,identification,learning,networks,neuralparameterplasticsratereinforced
(0)
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1Inverse Dirichlet weighting enables reliable training of physics informed neural networks
S. Maddu, D. Sturm, {. Mueller, and {. Sbalzarini. Machine learning: science and technology, (Feb 15, 2022)Funding Information: This work was supported by the German Research Foundation (DFG) – EXC-2068, Cluster of Excellence ‘Physics of Life’, and by the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, funded by the Federal Ministry of Education and Research (BMBF). The present work was also partly funded by the Center for Advanced Systems Understanding (CASUS), which is financed by Germany’s Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament. Publisher Copyright: © 2022 The Author(s). Published by IOP Publishing Ltd..
19 days ago by @scadsfct
show all tags
topic_lifescience
ALGORITHM
FIS_scads
active
catastrophic
flow
forgetting,
gradient
modeling,
multi-objective
multi-scale
networks,
neural
physics-informed
regularization,
training,
turbulence,
topic_lifescienceALGORITHMFIS_scadsactivecatastrophicflowforgetting,gradientmodeling,multi-objectivemulti-scalenetworks,neuralphysics-informedregularization,training,turbulence,
(0)
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1Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems
S. Perez, S. Maddu, {. Sbalzarini, and P. Poncet. Journal of computational physics, (Oct 15, 2023)Publisher Copyright: © 2023 The Author(s).
19 days ago by @scadsfct
show all tags
topic_lifescience
Adaptive
Artificial
Bayesian
Carlo,
FIS_scads
Hamiltonian
Intelligence,
Monte
Multi-objective
Quantification
Uncertainty
learning,
networks,
neural
physics-informed
training,
weight
topic_lifescienceAdaptiveArtificialBayesianCarlo,FIS_scadsHamiltonianIntelligence,MonteMulti-objectiveQuantificationUncertaintylearning,networks,neuralphysics-informedtraining,weight
(0)
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1SetQuence & SetOmic: Deep Set Transformer-based Representations of Cancer Multi-Omics
N. Jurenaite, D. Leon-Perinan, V. Donath, S. Torge, and R. Jakel. 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022, page 139--147. United States of America, IEEE, New York u. a., (2022)Publisher Copyright: © 2022 IEEE.; 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022 ; Conference date: 15-08-2022 Through 17-08-2022.
19 days ago by @scadsfct
show all tags
topic_federatedlearn
livinglab
Deep
FIS_scads
Network,
Neural
Representations
Set
analysis,
expression,
gene
genome,
language
molecular
multi-omics,
mutome,
natural
processing,
sequence
topic_federatedlearnlivinglabDeepFIS_scadsNetwork,NeuralRepresentationsSetanalysis,expression,genegenome,languagemolecularmulti-omics,mutome,naturalprocessing,sequence
(0)
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1Artificial neural network small-sample-bias-corrections of the AR(1) parameter close to unit root
H. Jiang, O. Okhrin, and M. Rockinger. Statistica Neerlandica, (Jul 31, 2024)Publisher Copyright: © 2024 The Author(s). Statistica Neerlandica published by John Wiley & Sons Ltd on behalf of Netherlands Society for Statistics and Operations Research..
19 days ago by @scadsfct
show all tags
topic_engineering
FIS_scads
bias
correction,
network,
neural
sample
small
topic_engineeringFIS_scadsbiascorrection,network,neuralsamplesmall
(0)
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1Spatial–temporal recurrent reinforcement learning for autonomous ships
M. Waltz, and O. Okhrin. Neural Networks, 2023 (165): 634--653 (Jun 15, 2023)Publisher Copyright: © 2023 Elsevier Ltd.
19 days ago by @scadsfct
show all tags
topic_engineering
Algorithms,
Autonomous
COLREG,
Computer,
Deep
FIS_scads
Networks,
Neural
Psychology,
Recurrency,
Reinforcement,
Reward
Ships,
learning,
reinforcement
surface
vehicle,
topic_engineeringAlgorithms,AutonomousCOLREG,Computer,DeepFIS_scadsNetworks,NeuralPsychology,Recurrency,Reinforcement,RewardShips,learning,reinforcementsurfacevehicle,
(0)
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1Learning computable models from data
S. Maddu, D. Sturm, {. Cheeseman, {. Müller, and {. Sbalzarini. (2021)Publisher Copyright: © 2021, Univelt Inc., All rights reserved.; 14th World Congress of Computational Mechanics and ECCOMAS Congress, WCCM-ECCOMAS 2020 ; Conference date: 11-01-2021 Through 15-01-2021.
19 days ago by @scadsfct
show all tags
Differential
ENO-WENO,
FIS_scads
Neural
Surrogate
modeling
networks,
operators,
DifferentialENO-WENO,FIS_scadsNeuralSurrogatemodelingnetworks,operators,
(0)
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1OmniOpt – A tool for hyperparameter optimization on HPC
P. Winkler, N. Koch, A. Hornig, and J. Gerritzen. High Performance Computing - ISC High Performance Digital 2021 International Workshops, 2021, Revised Selected Papers, page 285--296. Germany, Springer, Berlin u. a., (Nov 13, 2021)Publisher Copyright: © 2021, Springer Nature Switzerland AG.; International Conference on High Performance Computing, ISC High Performance 2021 ; Conference date: 24-06-2021 Through 02-07-2021.
19 days ago by @scadsfct
show all tags
FIS_scads
High
Hyperparameter
Neural
computing,
networks
optimization,
performance
FIS_scadsHighHyperparameterNeuralcomputing,networksoptimization,performance
(0)
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1Inadequacy of common stochastic neural networks for reliable clinical decision support
A. Lindenmeyer, M. Blattmann, S. Franke, T. Neumuth, and D. Schneider. (2024)
7 days ago by @scadsfct
show all tags
Inadequacy
clinical
decision
networks
neural
reliable
stochastic
support
Inadequacyclinicaldecisionnetworksneuralreliablestochasticsupport
(0)
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2Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks
A. Marcolongo, M. Vladymyrov, S. Lienert, N. Peleg, S. Haug, and J. Zscheischler. Environmental Data Science, (2022)
7 days ago by @scadsfct
show all tags
Convolutional
Networks
Neural
Predicting
data
gross
low
primary
production
weather
ConvolutionalNetworksNeuralPredictingdatagrosslowprimaryproductionweather
(0)
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publications (hide)20
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1Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network

1Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach

1Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review

1Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology

1Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction

1Introduction to the BioChemical Library (BCL): An application-based open-source toolkit for integrated cheminformatics and machine learning in computer-aided drug discovery

1Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks

1TraceVis: Towards Visualization for Deep Statistical Model Checking

1Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks

1A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP

1Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning

1Inverse Dirichlet weighting enables reliable training of physics informed neural networks

1Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems

1SetQuence & SetOmic: Deep Set Transformer-based Representations of Cancer Multi-Omics

1Artificial neural network small-sample-bias-corrections of the AR(1) parameter close to unit root

1Spatial–temporal recurrent reinforcement learning for autonomous ships

1Learning computable models from data

1OmniOpt – A tool for hyperparameter optimization on HPC

1Inadequacy of common stochastic neural networks for reliable clinical decision support

2Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks

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