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publications (hide)27
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1Extendable hybrid approach to detect conscious states in a CLIS patient using machine learning
S. Adama, S. Wu, N. Nicolaou, and M. Bogdan. SNE Simul. Notes Eur., 32 (1): 37--45 (2022)
8 months ago by @scadsfct
show all tags
hybrid
machine
patient
{CLIS}
learning
zno
states
conscious
hybridmachinepatient{CLIS}learningznostatesconscious
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1Deep learning improves pancreatic cancer diagnosis using RNA-based variants
A. Al-Fatlawi, N. Malekian, S. Garc\'ıa, A. Henschel, I. Kim, A. Dahl, B. Jahnke, P. Bailey, S. Bolz, A. Poetsch and 4 other author(s). Cancers (Basel), 13 (11): 2654 (May 2021)
12 months ago by @scadsfct
show all tags
study
transcriptome-wide
deep
cancer
topic_lifescience
association
learning
pancreatic
pancreatitis
zno
chronic
studytranscriptome-widedeepcancertopic_lifescienceassociationlearningpancreaticpancreatitisznochronic
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1Key Point Analysis via Contrastive Learning and Extractive Argument Summarization
M. Alshomary, T. Gurcke, S. Syed, P. Heinisch, M. Spliethöver, P. Cimiano, M. Potthast, and H. Wachsmuth. 8th Workshop on Argument Mining (ArgMining 2021) at EMNLP, page 184--189. Association for Computational Linguistics, (November 2021)
8 months ago by @scadsfct
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Analysis
Point
Argument
learning
Extractive
zno
Contrastive
Key
Summarization
AnalysisPointArgumentlearningExtractiveznoContrastiveKeySummarization
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1BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification
A. Avila Santos, B. de Almeida, R. Bonidia, P. Stadler, P. Stefanic, I. Mandic-Mulec, U. Rocha, D. Sanches, and A. de Carvalho. RNA Biology, 21 (1): 410–421 (March 2024)
9 months ago by @scadsfct
show all tags
deep
RNA
non-coding
feature
learning
zno
extraction
BioDeepfuse
classification
deepRNAnon-codingfeaturelearningznoextractionBioDeepfuseclassification
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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)
12 months ago by @scadsfct
show all tags
metastasis;
bowel
predictive
adipose
intelligence
digital
biomarker
artificial
prediction
zno
tissue;
pathology
inflamed
deep
cancer
learning
LNM
pT2
pT1
AI;
colorectal
and
topic_lifescience
new
early
metastasis;bowelpredictiveadiposeintelligencedigitalbiomarkerartificialpredictionznotissue;pathologyinflameddeepcancerlearningLNMpT2pT1AI;colorectalandtopic_lifesciencenewearly
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1Sampling Bias Due to Near-Duplicates in Learning to Rank
M. Fröbe, J. Bevendorff, J. Reimer, M. Potthast, and M. Hagen. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, page 1997–2000. New York, NY, USA, Association for Computing Machinery, (2020)
11 months ago by @scadsfct
show all tags
selection
principle
novelty
bias
learning
to
zno
near-duplicate-detection
rank
selectionprinciplenoveltybiaslearningtoznonear-duplicate-detectionrank
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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)
12 months ago by @scadsfct
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Convolutional
pathology
Multiple-Instance
deep
neural
learning
networks
Weakly-supervised
intelligence
Learning;
artificial
transformers;
topic_lifescience
Vision
zno
Computational
ConvolutionalpathologyMultiple-InstancedeepneurallearningnetworksWeakly-supervisedintelligenceLearning;artificialtransformers;topic_lifescienceVisionznoComputational
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1Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk
F. Hart, and O. Okhrin. Neurocomputing, (2024)
10 months ago by @scadsfct
show all tags
Training
Dynamic
environment
Collision
Reinforcement
learning
metric
risk
zno
avoidance
obstacle
topic_engineering
TrainingDynamicenvironmentCollisionReinforcementlearningmetricriskznoavoidanceobstacletopic_engineering
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1Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study
M. Huemer, A. Bauer, A. Petrera, M. Scholz, S. Hauck, M. Drey, A. Peters, and B. Thorand. J. Cachexia Sarcopenia Muscle, 12 (4): 1011--1023 (August 2021)
12 months ago by @scadsfct
show all tags
mass
index;
skeletal
learning
Appendicular
mass;
Proteomics
muscle
machine
Fat
fat
Muscle
zno
Body
massindex;skeletallearningAppendicularmass;ProteomicsmusclemachineFatfatMuscleznoBody
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1Automated algorithm selection: Survey and perspectives
P. Kerschke, H. Hoos, F. Neumann, and H. Trautmann. Evol. Comput., 27 (1): 3--45 (2019)
12 months ago by @scadsfct
show all tags
feature-based
metalearning
configuration
streams
data
approaches
learning
analysis
automated
exploratory
selection
optimisation
combinatorial
continuous
machine
zno
algorithm
landscape
feature-basedmetalearningconfigurationstreamsdataapproacheslearninganalysisautomatedexploratoryselectionoptimisationcombinatorialcontinuousmachineznoalgorithmlandscape
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1Automated algorithm selection on continuous black-box problems by combining Exploratory Landscape Analysis and machine learning
P. Kerschke, and H. Trautmann. Evol. Comput., 27 (1): 99--127 (2019)
12 months ago by @scadsfct
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single-objective
learning
black-box
analysis
automated
exploratory
selection
optimization
continuous
machine
zno
algorithm
landscape
single-objectivelearningblack-boxanalysisautomatedexploratoryselectionoptimizationcontinuousmachineznoalgorithmlandscape
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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)
8 months ago by @scadsfct
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models
modeling
Theory
Consciousness
algorithms
Prediction
data
learning
Information
Modeling
Complexity
Processing
Syndrome
Training
Neuroscience
Measures
Predictive
and
machine
Locked-In
processing
zno
Signal
Computational
modelsmodelingTheoryConsciousnessalgorithmsPredictiondatalearningInformationModelingComplexityProcessingSyndromeTrainingNeuroscienceMeasuresPredictiveandmachineLocked-InprocessingznoSignalComputational
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1Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective
R. Knauer, and E. Rodner. KI 2023: Advances in Artificial Intelligence: 46th German Conference on AI, Berlin, Germany, September 26--29, 2023, Proceedings, page 114--129. Berlin, Heidelberg, Springer-Verlag, (2023)
8 months ago by @scadsfct
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mixed-integer
selection
optimization
meta-learning
machine
learning
zno
conic
interpretable
cost-sensitive
best
subset
mixed-integerselectionoptimizationmeta-learningmachinelearningznoconicinterpretablecost-sensitivebestsubset
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1Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging
L. Lampe, H. Huppertz, S. Anderl-Straub, F. Albrecht, T. Ballarini, S. Bisenius, K. Mueller, S. Niehaus, K. Fassbender, K. Fliessbach and 13 other author(s). NeuroImage Clin., 37 (103320): 103320 (January 2023)
12 months ago by @scadsfct
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MRI
Neurodegeneration
unit_test
Volumetry
machine
topic_lifescience
topic_neuroinspired
learning
Diagnosis
zno
Dementia
MRINeurodegenerationunit_testVolumetrymachinetopic_lifesciencetopic_neuroinspiredlearningDiagnosisznoDementia
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1Predicting mutational status of driver and suppressor genes directly from histopathology with Deep Learning: A systematic study across 23 solid tumor types
C. Loeffler, N. Gaisa, H. Muti, M. van Treeck, A. Echle, N. Ghaffari Laleh, C. Trautwein, L. Heij, H. Grabsch, N. Ortiz Bruechle and 1 other author(s). Front. Genet., (2021)
12 months ago by @scadsfct
show all tags
genetic
artificail
(AI);
pathway;
deep
cancer
genes;
learning
pathway
zno
TCGA;
intelligence
geneticartificail(AI);pathway;deepcancergenes;learningpathwayznoTCGA;intelligence
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1Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: A possible preselection for molecular testing?
C. Loeffler, N. Ortiz Bruechle, M. Jung, L. Seillier, M. Rose, N. Laleh, R. Knuechel, T. Brinker, C. Trautwein, N. Gaisa and 1 other author(s). Eur. Urol. Focus, 8 (2): 472--479 (March 2022)
12 months ago by @scadsfct
show all tags
deep
cancer
therapy
for
learning
intelligence
Bladder
mutations
artificial
Molecular
fibroblast
receptor
testing
topic_lifescience
growth
zno
factor
FGFR3
deepcancertherapyforlearningintelligenceBladdermutationsartificialMolecularfibroblastreceptortestingtopic_lifesciencegrowthznofactorFGFR3
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1Automatic lung segmentation and quantification of aeration in computed tomography of the chest using 3D transfer learning
L. Maiello, L. Ball, M. Micali, F. Iannuzzi, N. Scherf, R. Hoffmann, M. Gama de Abreu, P. Pelosi, and R. Huhle. Front. Physiol., (2021)
12 months ago by @scadsfct
show all tags
ARDS
uNet
transfer
recruitment
lung
deep
learning
index
segmentation
zno
COVID-19
Jaccard
ARDSuNettransferrecruitmentlungdeeplearningindexsegmentationznoCOVID-19Jaccard
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1Model soups improve performance of dermoscopic skin cancer classifiers
R. Maron, A. Hekler, S. Haggenmüller, C. von Kalle, J. Utikal, V. Müller, M. Gaiser, F. Meier, S. Hobelsberger, F. Gellrich and 23 other author(s). Eur. J. Cancer, (September 2022)
12 months ago by @scadsfct
show all tags
Dermatology
Melanoma
deep
Nevus
Calibration
learning
Ensembles
intelligence
artificial
soups
Robustness
topic_lifescience
Model
zno
Generalisation
DermatologyMelanomadeepNevusCalibrationlearningEnsemblesintelligenceartificialsoupsRobustnesstopic_lifescienceModelznoGeneralisation
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1GradTree: Learning axis-aligned decision trees with gradient descent
S. Marton, S. Lüdtke, C. Bartelt, und H. Stuckenschmidt. (2023)
vor 8 Monaten von @scadsfct
alle anzeigen
descent
axis-aligned
GradTree
decision
gradient
learning
zno
trees
descentaxis-alignedGradTreedecisiongradientlearningznotrees
KopierenLöschenDiese Publikation zur Ablage hinzufügen
1PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning
L. Monteiro, J. Saraiva, R. Toscan, P. Stadler, R. Silva-Rocha, und U. Nunes da Rocha. Environmental Microbiome, (December 2022)
vor 11 Monaten von @scadsfct
alle anzeigen
microbialdeep
bacterial
PredicTF:
transcription
complex
prediction
learning
zno
microbialdeepbacterialPredicTF:transcriptioncomplexpredictionlearningzno
KopierenLöschenDiese Publikation zur Ablage hinzufügen

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publications (hide)27
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1Extendable hybrid approach to detect conscious states in a CLIS patient using machine learning

1Deep learning improves pancreatic cancer diagnosis using RNA-based variants

1Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

1BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification

1Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer

1Sampling Bias Due to Near-Duplicates in Learning to Rank

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

1Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk

1Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study

1Automated algorithm selection: Survey and perspectives

1Automated algorithm selection on continuous black-box problems by combining Exploratory Landscape Analysis and machine learning

1Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient

1Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective

1Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

1Predicting mutational status of driver and suppressor genes directly from histopathology with Deep Learning: A systematic study across 23 solid tumor types

1Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: A possible preselection for molecular testing?

1Automatic lung segmentation and quantification of aeration in computed tomography of the chest using 3D transfer learning

1Model soups improve performance of dermoscopic skin cancer classifiers

1GradTree: Learning axis-aligned decision trees with gradient descent

1PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning

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