Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Avetisian, Manvel"'
Autor:
Tsvigun, Akim, Shelmanov, Artem, Kuzmin, Gleb, Sanochkin, Leonid, Larionov, Daniil, Gusev, Gleb, Avetisian, Manvel, Zhukov, Leonid
Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models. Deep learning offers a solution for several essential obstacles to deploying AL in practice but introduces many others. On
Externí odkaz:
http://arxiv.org/abs/2205.03598
Autor:
Ponomarchuk, Alexander, Burenko, Ilya, Malkin, Elian, Nazarov, Ivan, Kokh, Vladimir, Avetisian, Manvel, Zhukov, Leonid
The COVID-19 pandemic created a significant interest and demand for infection detection and monitoring solutions. In this paper we propose a machine learning method to quickly triage COVID-19 using recordings made on consumer devices. The approach co
Externí odkaz:
http://arxiv.org/abs/2107.10716
Autor:
Avetisian, Manvel, Burenko, Ilya, Egorov, Konstantin, Kokh, Vladimir, Nesterov, Aleksandr, Nikolaev, Aleksandr, Ponomarchuk, Alexander, Sokolova, Elena, Tuzhilin, Alex, Umerenkov, Dmitry
Analysis of chest CT scans can be used in detecting parts of lungs that are affected by infectious diseases such as COVID-19.Determining the volume of lungs affected by lesions is essential for formulating treatment recommendations and prioritizingpa
Externí odkaz:
http://arxiv.org/abs/2105.11863
Holter monitoring, a long-term ECG recording (24-hours and more), contains a large amount of valuable diagnostic information about the patient. Its interpretation becomes a difficult and time-consuming task for the doctor who analyzes them because ev
Externí odkaz:
http://arxiv.org/abs/2011.09303
In this paper we study the problem of predicting clinical diagnoses from textual Electronic Health Records (EHR) data. We show the importance of this problem in medical community and present comprehensive historical review of the problem and proposed
Externí odkaz:
http://arxiv.org/abs/2007.07562
Segmentation of ischemic stroke and intracranial hemorrhage on computed tomography is essential for investigation and treatment of stroke. In this paper, we modified the U-Net CNN architecture for the stroke identification problem using non-contrast
Externí odkaz:
http://arxiv.org/abs/2003.14287
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Akademický článek
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