Murat Seçkin Ayhan
Murat Seçkin Ayhan
Institute for Ophthalmic Research, University of Tübingen
Email verificata su uni-tuebingen.de - Home page
Titolo
Citata da
Citata da
Anno
Leveraging uncertainty information from deep neural networks for disease detection
C Leibig, V Allken, MS Ayhan, P Berens, S Wahl
Scientific Reports 7, 2017
2142017
Natural Image Bases to Represent Neuroimaging Data
A Gupta, MS Ayhan, AS Maida
The 30th International Conference on Machine Learning (ICML 2013), 2013
1772013
Test-time Data Augmentation for Estimation of Heteroscedastic Aleatoric Uncertainty in Deep Neural Networks
MS Ayhan, P Berens
International conference on Medical Imaging with Deep Learning (MIDL) 2018, 2018
532018
Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection
MS Ayhan, L Kühlewein, G Aliyeva, W Inhoffen, F Ziemssen, P Berens
Medical Image Analysis 64, 101724, 2020
112020
Exploitation of 3D stereotactic surface projection for predictive modelling of Alzheimer’s disease
MS Ayhan, RG Benton, VV Raghavan, S Choubey
International journal of data mining and bioinformatics 7 (2), 146-165, 2013
62013
Composite kernels for automatic relevance determination in computerized diagnosis of Alzheimer’s disease
MS Ayhan, RG Benton, VV Raghavan, S Choubey
International Conference on Brain and Health Informatics, 126-137, 2013
52013
Exploitation of 3D Stereotactic Surface Projection for automated classification of Alzheimer's disease according to dementia levels
MS Ayhan, RG Benton, VV Raghavan, S Choubey
2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2010
52010
Proprietary data formats block health research
P Berens, MS Ayhan
Nature 565 (7737), 429-430, 2019
42019
Utilization of domain-knowledge for simplicity and comprehensibility in predictive modeling of Alzheimer's disease
MS Ayhan, RG Benton, VV Raghavan, S Choubey
2012 IEEE International Conference on Bioinformatics and Biomedicine …, 2012
42012
Potential of methods of artificial intelligence for quality assurance
P Berens, SM Waldstein, MS Ayhan, L Kuemmerle, H Agostini, A Stahl, ...
Der Ophthalmologe: Zeitschrift der Deutschen Ophthalmologischen Gesellschaft …, 2020
2*2020
Evaluation of Autoencoders for Bases to Represent Neuroimaging Data
A Gupta, MS Ayhan, AS Maida
NIPS 2013 Workshop on Machine Learning and Interpretation in NeuroImaging, 2013
22013
Towards Indefinite Gaussian Processes
MS Ayhan, CHH Chu
NIPS 2012 Modern Nonparametric Methods in Machine Learning Workshop, 2012
22012
Multiple kernel learning and automatic subspace relevance determination for high-dimensional neuroimaging data
MS Ayhan, V Raghavan
arXiv preprint arXiv:1706.00856, 2017
12017
Clinical Validation of Saliency Maps for Understanding Deep Neural Networks in Ophthalmology
MS Ayhan, LB Kümmerle, L Kühlewein, W Inhoffen, G Aliyeva, ...
medRxiv, 2021
2021
Efficient and Automatic Subspace Relevance Determination via Multiple Kernel Learning for High-Dimensional Neuroimaging Data
MS Ayhan, V Raghavan
International Conference on Brain Informatics, 226-238, 2018
2018
A Probabilistic Biomarker for Alzheimer's Disease
MS Ayhan
University of Louisiana at Lafayette, 2015
2015
Determining Relevant Features based on 3D-SSP to Detect Dementia Caused by Alzheimer's Disease
MS Ayhan, RG Benton, VV Raghavan, S Choubey
Biotechnology and Bioinformatics Symposium (BIOT-2010), 2010
2010
Leveraging uncertainty information from deep neural networks for disease detection Open Website
C Leibig, V Allken, MS Ayhan, P Berens, S Wahl
Evaluation of Autoencoders for Bases to Represent Neuroimaging Data Open Website
A Gupta, MS Ayhan, AS Maida
Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection Open Website
MS Ayhan, L Kühlewein, G Aliyeva, W Inhoffen, F Ziemssen, P Berens
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–20