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Synergy of physics-based reasoning and machine learning in biomedical applications: towards unlimited deep learning with limited data

Advances in physics: X, 2019-01, Vol.4 (1), p.1582361

2019

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Segmentation of blurred objects by classification of isolabel contours

Pattern recognition, 2014-12, Vol.47 (12), p.3881-3889

Senyukova, Olga V.

2014

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Automated Diagnostic Model Based on Isoline Map Analysis of Myocardial Tissue Structure

Computational Intelligence, 2019, Vol.829, p.221-238

2019

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Segmentation of blurred objects by classification of isolabel contours

Pattern recognition, 2014, Vol.47 (12), p.3881-3889

SENYUKOVA, Olga V

2014

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Right Ventricle Segmentation in Cardiac MR Images Using U-Net with Partly Dilated Convolution

Artificial Neural Networks and Machine Learning – ICANN 2018, p.179-185

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Multi-complexity ensemble measures for gait time series analysis: application to diagnostics, monitoring and biometrics

Advances in experimental medicine and biology, 2015, Vol.823, p.107

2015

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Advantages of Hybrid Deep Learning Frameworks in Applications with Limited Data

International Journal of Machine Learning and Computing, 2018-12, Vol.8 (6)

2018

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MRI Quality Control Algorithm Based on Image Analysis Using Convolutional and Recurrent Neural Networks

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), 2022, p.1-4

2022

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Generic Ensemble-Based Representation of Global Cardiovascular Dynamics for Personalized Treatment Discovery and Optimization

Computational Collective Intelligence, p.197-207

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Fast Brain MRI Registration with Automatic Landmark Detection Using a Single Template Image

Pattern Recognition, p.390-399

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Universal Multi-complexity Measures for Physiological State Quantification in Intelligent Diagnostics and Monitoring Systems

Biomedical Informatics and Technology, p.76-90

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Discovery of Hybrid Ensemble Models Resilient to Input Resolution Deterioration

2021 4th International Conference on Information and Computer Technologies (ICICT), 2021, p.253-259

2021

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Leveraging Neural-Networks, Boosting and Domain-Knowledge to Discover Physiological Indicators with Minimal Sensitivity to Data Resolution

2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), 2022, p.214-219

2022

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Multi-expert evolving system for objective psychophysiological monitoring and fast discovery of effective personalized therapies

2017 Evolving and Adaptive Intelligent Systems (EAIS), 2017, p.1-8

2017

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Synergy of physics-based reasoning and machine learning in biomedical applications: towards unlimited deep learning with limited data

Segmentation of blurred objects by classification of isolabel contours

Automated Diagnostic Model Based on Isoline Map Analysis of Myocardial Tissue Structure

Segmentation of blurred objects by classification of isolabel contours

Right Ventricle Segmentation in Cardiac MR Images Using U-Net with Partly Dilated Convolution

Multi-complexity ensemble measures for gait time series analysis: application to diagnostics, monitoring and biometrics

Advantages of Hybrid Deep Learning Frameworks in Applications with Limited Data

MRI Quality Control Algorithm Based on Image Analysis Using Convolutional and Recurrent Neural Networks

Generic Ensemble-Based Representation of Global Cardiovascular Dynamics for Personalized Treatment Discovery and Optimization

Fast Brain MRI Registration with Automatic Landmark Detection Using a Single Template Image

Universal Multi-complexity Measures for Physiological State Quantification in Intelligent Diagnostics and Monitoring Systems

Discovery of Hybrid Ensemble Models Resilient to Input Resolution Deterioration

Leveraging Neural-Networks, Boosting and Domain-Knowledge to Discover Physiological Indicators with Minimal Sensitivity to Data Resolution

Multi-expert evolving system for objective psychophysiological monitoring and fast discovery of effective personalized therapies

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