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publications (hide)19
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2Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning
L. Lange, M. Heykeroth, and E. Rahm. arXiv preprint arXiv:2409.01329, (September 2024)
3 months ago by @scadsfct
show all tags
ep
FOS:
Machine
sciences
(cs.LG),
Security
Learning
(cs.CV),
and
Vision
Computer
(cs.CR),
Cryptography
information
Pattern
area_bigdata
area_responsibleai
Recognition
epFOS:Machinesciences(cs.LG),SecurityLearning(cs.CV),andVisionComputer(cs.CR),CryptographyinformationPatternarea_bigdataarea_responsibleaiRecognition
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2Content-Aware Depth-Adaptive Image Restoration
T. Vargis, and S. Ghiasvand. Proceedings of the 29th International Conference on Automation and Computing, Sunderland, UK, (January 2024)arXiv:2401.05049 cs.
3 months ago by @ghiasvan
show all tags
Learning,
myOwn
and
Vision
Computer
Pattern
Science
Machine
Recognition,
Learning,myOwnandVisionComputerPatternScienceMachineRecognition,
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1If there's a Trigger Warning, then where's the Trigger? Investigating Trigger Warnings at the Passage Level
M. Wiegmann, J. Rakete, M. Wolska, B. Stein, and M. Potthast. (2024)
5 months ago by @scadsfct
show all tags
sciences,
FOS:
Computers
sciences
(cs.CY),
Computation
topic_language
Society
and
Computer
Language
(cs.CL),
information
sciences,FOS:Computerssciences(cs.CY),Computationtopic_languageSocietyandComputerLanguage(cs.CL),information
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2Explainable Earth Surface Forecasting under Extreme Events
O. Pellicer-Valero, M. Fernández-Torres, C. Ji, M. Mahecha, and G. Camps-Valls. (2024)
3 months ago by @joum576e
show all tags
sciences
(cs.LG),
Learning
topic_earthenvironment
and
Computer
sciences,
information
FOS:
Machine
sciences(cs.LG),Learningtopic_earthenvironmentandComputersciences,informationFOS:Machine
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1Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries
A. de Carvalho, R. Bonidia, J. Kong, M. Dauhajre, C. Struchiner, G. Goedert, P. Stadler, M. Walter, D. Sanches, T. Day and 9 other author(s). (2024)
2 months ago by @scadsfct
show all tags
sciences
Artificial
Intelligence
and
FOS
Computer
Zno
information
sciencesArtificialIntelligenceandFOSComputerZnoinformation
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1The Rad52 SSAP superfamily and new insight into homologous recombination
A. Al-Fatlawi, M. Schroeder, and {. Stewart. Communications biology, (Jan 23, 2023)Funding Information: We gratefully acknowledge support from ZIH of TU Dresden and the BMBF Project scads.ai..
3 months ago by @scadsfct
show all tags
FIS_scads
Rad52
Recombination
DNA
and
topic_lifescience
Protein/genetics
Recombination,
Repair
Homologous
FIS_scadsRad52RecombinationDNAandtopic_lifescienceProtein/geneticsRecombination,RepairHomologous
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1Mining the History Sections of Wikipedia Articles on Science and Technology
W. Kircheis, M. Schmidt, A. Simons, B. Stein, and M. Potthast. 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL), page 200-204. (2023)
2 months ago by @scadsfct
show all tags
Filtering
innovation
Priority
Encyclopedias
Transforms
Wikipedia
Science
Studies
Disputes
Technology
Innovation
Focusing
and
Internet
Libraries
Zno
Technological
FilteringinnovationPriorityEncyclopediasTransformsWikipediaScienceStudiesDisputesTechnologyInnovationFocusingandInternetLibrariesZnoTechnological
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1Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient
O. Kirsten, M. Bogdan, and S. Adama. 2023 7th International Conference on Imaging, Signal Processing and Communications (ICISPC), page 37-41. (2023)
2 months ago by @scadsfct
show all tags
models
Consciousness
Prediction
Syndrome
Predictive
processing
modeling
Theory
algorithms
data
learning
Information
Modeling
Complexity
Processing
Machine
Learning
Training
Neuroscience
Measures
and
Locked-In
Zno
Signal
Computational
modelsConsciousnessPredictionSyndromePredictiveprocessingmodelingTheoryalgorithmsdatalearningInformationModelingComplexityProcessingMachineLearningTrainingNeuroscienceMeasuresandLocked-InZnoSignalComputational
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1Content-adaptive generation and parallel compositing of volumetric depth images for responsive visualization of large volume data
A. Gupta, P. Incardona, P. Hunt, G. Reina, S. Frey, S. Gumhold, U. Günther, and {. Sbalzarini. WorkingPaper, (Jun 29, 2022)
3 months ago by @scadsfct
show all tags
FIS_scads
computing,
concepts
Visualization,
Human-centered
Visualization
paradigms
and
techniques
topic_lifescience
theory,
topic_visualcomputing
FIS_scadscomputing,conceptsVisualization,Human-centeredVisualizationparadigmsandtechniquestopic_lifesciencetheory,topic_visualcomputing
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1Efficient Raycasting of View-Dependent Piecewise Constant Representations of Volumetric Data
A. Gupta, U. Günther, P. Incardona, G. Reina, S. Frey, S. Gumhold, and {. Sbalzarini. WorkingPaper, (Jun 17, 2022)
3 months ago by @scadsfct
show all tags
FIS_scads
computing,
concepts
Visualization,
Human-centered
Visualization
and
techniques
topic_lifescience
theory,
topic_visualcomputing
paradigms,
FIS_scadscomputing,conceptsVisualization,Human-centeredVisualizationandtechniquestopic_lifesciencetheory,topic_visualcomputingparadigms,
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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)
6 months ago by @scadsfct
show all tags
a
disease;
HCC,
or
intelligence;
liver
multivariable
non-alcoholic
multimodal
ML,
artificial
WSIs,
images;
prediction
support
integration
TACE,
DICOM,
AI,
network;
deep
neural
Digital
Diagnosis;
MVI,
transarterial
fatty
topic_lifescience
microvascular
convolutional
in
learning;
imaging;
Communications
invasion;
NAFLD,
chemoembolisation;
hepatocellular
Reporting
steatohepatitis;
Transparent
Individual
slide
machine
Prognosis
Artificial
data
for
NASH,
whole
Medicine;
TRIPOD,
of
CNN,
and
Imaging
carcinoma;
diagnostic
system;
model
adisease;HCC,orintelligence;livermultivariablenon-alcoholicmultimodalML,artificialWSIs,images;predictionsupportintegrationTACE,DICOM,AI,network;deepneuralDigitalDiagnosis;MVI,transarterialfattytopic_lifesciencemicrovascularconvolutionalinlearning;imaging;Communicationsinvasion;NAFLD,chemoembolisation;hepatocellularReportingsteatohepatitis;TransparentIndividualslidemachinePrognosisArtificialdataforNASH,wholeMedicine;TRIPOD,ofCNN,andImagingcarcinoma;diagnosticsystem;model
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1Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer
S. Brockmoeller, A. Echle, N. Ghaffari Laleh, S. Eiholm, M. Malmstrøm, T. Plato Kuhlmann, K. Levic, H. Grabsch, N. West, O. Saldanha and 7 other author(s). J. Pathol., 256 (3): 269--281 (March 2022)
6 months ago by @scadsfct
show all tags
metastasis;
inflamed
intelligence;
deep
learning;
bowel
predictive
LNM
adipose
pT2
pT1
digital
biomarker;
cancer;
artificial
AI;
pathology;
colorectal
and
topic_lifescience
prediction
new
tissue;
early
metastasis;inflamedintelligence;deeplearning;bowelpredictiveLNMadiposepT2pT1digitalbiomarker;cancer;artificialAI;pathology;colorectalandtopic_lifesciencepredictionnewtissue;early
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2A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement
T. Vargis, and S. Ghiasvand. The International Conference for High Performance Computing, Networking, Storage, and Analysis, Dallas, Texas, USA, (January 2022)arXiv:2402.05114 cs.
3 months ago by @ghiasvan
show all tags
Parallel,
myOwn
Learning
and
Computer
Cluster
Distributed,
Computing,
Science
Machine
Parallel,myOwnLearningandComputerClusterDistributed,Computing,ScienceMachine
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1Personalized structural biology reveals the molecular mechanisms underlying heterogeneous epileptic phenotypes caused by de novo KCNC2 variants
S. Mukherjee, T. Cassini, N. Hu, T. Yang, B. Li, W. Shen, C. Moth, D. Rinker, J. Sheehan, J. Cogan and 8 other author(s). Human Genetics and Genomics Advances, 3 (4): 100131 (2022)
5 months ago by @scadsfct
show all tags
developmental
variant,
disease
molecular
encephalopathy,
electrophysiology,
Diseases
simulations,
personalized
structural
rare
dynamics
interpretation,
epileptic
KCNC2,
and
topic_lifescience
DEE,
variant
Network,
Undiagnosed
biology,
developmentalvariant,diseasemolecularencephalopathy,electrophysiology,Diseasessimulations,personalizedstructuralraredynamicsinterpretation,epilepticKCNC2,andtopic_lifescienceDEE,variantNetwork,Undiagnosedbiology,
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1Modeling Proficiency with Implicit User Representations
K. Breitwieser, A. Lahnala, C. Welch, L. Flek, and M. Potthast. (2021)
5 months ago by @scadsfct
show all tags
sciences
Computation
and
Computer
sciences,
Language
(cs.CL),
information
FOS:
sciencesComputationandComputersciences,Language(cs.CL),informationFOS:
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1Classification of hyperspectral endocrine tissue images using support vector machines
M. Maktabi, H. Köhler, M. Ivanova, T. Neumuth, N. Rayes, L. Seidemann, R. Sucher, B. Jansen-Winkeln, I. Gockel, M. Barberio and 1 other author(s). Int. J. Med. Robot., 16 (5): 1--10 (October 2020)
6 months ago by @scadsfct
show all tags
intraoperative
thyroidectomy
imaged
guided
computer
neck;
surgery;
and
imaging;
assisted
head
intraoperativethyroidectomyimagedguidedcomputerneck;surgery;andimaging;assistedhead
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1Modeling and processing up-to-dateness of patient information in probabilistic therapy decision support
J. Gaebel, H. Wu, A. Oeser, M. Cypko, M. Stoehr, A. Dietz, T. Neumuth, S. Franke, and S. Oeltze-Jafra. Artif. Intell. Med., 104 (101842): 101842 (April 2020)
6 months ago by @scadsfct
show all tags
Therapy
decision
modules;
Decision
neck
delay;
Arden
and
logic
system;
Medical
model
syntax;
oncology;
support
Head
Therapydecisionmodules;Decisionneckdelay;Ardenandlogicsystem;Medicalmodelsyntax;oncology;supportHead
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1Stability selection enables robust learning of partial differential equations from limited noisy data
S. Maddu, B. Cheeseman, I. Sbalzarini, and C. Müller. (2019)
5 months ago by @scadsfct
show all tags
(physics.data-an),
Analysis,
Probability
Data
sciences,
Numerical
Statistics
FOS:
Machine
Physical
sciences
Analysis
(cs.LG),
Learning
(math.NA),
Mathematics,
and
Computer
information
(physics.data-an),Analysis,ProbabilityDatasciences,NumericalStatisticsFOS:MachinePhysicalsciencesAnalysis(cs.LG),Learning(math.NA),Mathematics,andComputerinformation
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1Graph Mining for Complex Data Analytics
A. Petermann, M. Junghanns, S. Kemper, K. Gómez, N. Teichmann, and E. Rahm. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), page 1316--1319. (December 2016)
5 months ago by @scadsfct
show all tags
analysis;Data
algorithms;Conferences;Graph
design
Intelligence
and
Data
mining;Business;Algorithm
Mining;Business
models;Libraries;Partitioning
analysis;Dataalgorithms;Conferences;GraphdesignIntelligenceandDatamining;Business;AlgorithmMining;Businessmodels;Libraries;Partitioning
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