Zobrazeno 1 - 10
of 87
pro vyhledávání: '"A Dalechina"'
Autor:
Kurmukov, Anvar, Zavolovich, Bogdan, Dalechina, Aleksandra, Proskurov, Vladislav, Shirokikh, Boris
Image compression is a critical tool in decreasing the cost of storage and improving the speed of transmission over the internet. While deep learning applications for natural images widely adopts the usage of lossy compression techniques, it is not w
Externí odkaz:
http://arxiv.org/abs/2409.16733
Autor:
Kondrateva, Ekaterina, Druzhinina, Polina, Dalechina, Alexandra, Zolotova, Svetlana, Golanov, Andrey, Shirokikh, Boris, Belyaev, Mikhail, Kurmukov, Anvar
Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability. A conventional way to mitigate MR image heterogeneity is to apply preprocessing transformations such
Externí odkaz:
http://arxiv.org/abs/2204.05278
Autor:
Kondrateva, Ekaterina, Druzhinina, Polina, Dalechina, Alexandra, Zolotova, Svetlana, Golanov, Andrey, Shirokikh, Boris, Belyaev, Mikhail, Kurmukov, Anvar
Publikováno v:
In Biomedical Signal Processing and Control October 2024 96 Part A
Autor:
Shirokikh, Boris, Dalechina, Alexandra, Shevtsov, Alexey, Krivov, Egor, Kostjuchenko, Valery, Durgaryan, Amayak, Galkin, Mikhail, Golanov, Andrey, Belyaev, Mikhail
We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our segmentation method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow. With our method, we address the
Externí odkaz:
http://arxiv.org/abs/2108.09535
Autor:
Shirokikh, Boris, Shevtsov, Alexey, Kurmukov, Anvar, Dalechina, Alexandra, Krivov, Egor, Kostjuchenko, Valery, Golanov, Andrey, Belyaev, Mikhail
Target imbalance affects the performance of recent deep learning methods in many medical image segmentation tasks. It is a twofold problem: class imbalance - positive class (lesion) size compared to negative class (non-lesion) size; lesion size imbal
Externí odkaz:
http://arxiv.org/abs/2007.10033
Autor:
Pimkin, Artem, Samoylenko, Alexander, Antipina, Natalia, Ovechkina, Anna, Golanov, Andrey, Dalechina, Alexandra, Belyaev, Mikhail
Recent CT Metal Artifacts Reduction (MAR) methods are often based on image-to-image convolutional neural networks for adjustment of corrupted sinograms or images themselves. In this paper, we are exploring the capabilities of a multi-domain method wh
Externí odkaz:
http://arxiv.org/abs/1911.05530
Autor:
Shirokikh, Boris, Dalechina, Alexandra, Shevtsov, Alexey, Krivov, Egor, Kostjuchenko, Valery, Durgaryan, Amayak, Galkin, Mikhail, Osinov, Ivan, Golanov, Andrey, Belyaev, Mikhail
Stereotactic radiosurgery is a minimally-invasive treatment option for a large number of patients with intracranial tumors. As part of the therapy treatment, accurate delineation of brain tumors is of great importance. However, slice-by-slice manual
Externí odkaz:
http://arxiv.org/abs/1909.02799
Akademický článek
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Autor:
Pisov, Maxim, Makarchuk, Gleb, Kostjuchenko, Valery, Dalechina, Alexandra, Golanov, Andrey, Belyaev, Mikhail
Classification-based image retrieval systems are built by training convolutional neural networks (CNNs) on a relevant classification problem and using the distance in the resulting feature space as a similarity metric. However, in practical applicati
Externí odkaz:
http://arxiv.org/abs/1810.09369
Autor:
Krivov, Egor, Kostjuchenko, Valery, Dalechina, Alexandra, Shirokikh, Boris, karchuk, Gleb, Denisenko, Alexander, Golanov, Andrey, Belyaev, Mikhail
Deep learning methods are actively used for brain lesion segmentation. One of the most popular models is DeepMedic, which was developed for segmentation of relatively large lesions like glioma and ischemic stroke. In our work, we consider segmentatio
Externí odkaz:
http://arxiv.org/abs/1808.00244