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Recognition of Tomographic Images in the Diagnosis of Stroke. / Kalmutskiy, Kirill; Tulupov, Andrey; Berikov, Vladimir.

Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings. ред. / Alberto Del Bimbo; Rita Cucchiara; Stan Sclaroff; Giovanni Maria Farinella; Tao Mei; Marco Bertini; Hugo Jair Escalante; Roberto Vezzani. Springer, 2021. стр. 166-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 12665 LNCS).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

Harvard

Kalmutskiy, K, Tulupov, A & Berikov, V 2021, Recognition of Tomographic Images in the Diagnosis of Stroke. в A Del Bimbo, R Cucchiara, S Sclaroff, GM Farinella, T Mei, M Bertini, HJ Escalante & R Vezzani (ред.), Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Том. 12665 LNCS, Springer, стр. 166-171, 25th International Conference on Pattern Recognition Workshops, ICPR 2020, Milan, Италия, 10.01.2021. https://doi.org/10.1007/978-3-030-68821-9_16

APA

Kalmutskiy, K., Tulupov, A., & Berikov, V. (2021). Recognition of Tomographic Images in the Diagnosis of Stroke. в A. Del Bimbo, R. Cucchiara, S. Sclaroff, G. M. Farinella, T. Mei, M. Bertini, H. J. Escalante, & R. Vezzani (Ред.), Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings (стр. 166-171). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 12665 LNCS). Springer. https://doi.org/10.1007/978-3-030-68821-9_16

Vancouver

Kalmutskiy K, Tulupov A, Berikov V. Recognition of Tomographic Images in the Diagnosis of Stroke. в Del Bimbo A, Cucchiara R, Sclaroff S, Farinella GM, Mei T, Bertini M, Escalante HJ, Vezzani R, Редакторы, Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings. Springer. 2021. стр. 166-171. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-68821-9_16

Author

Kalmutskiy, Kirill ; Tulupov, Andrey ; Berikov, Vladimir. / Recognition of Tomographic Images in the Diagnosis of Stroke. Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings. Редактор / Alberto Del Bimbo ; Rita Cucchiara ; Stan Sclaroff ; Giovanni Maria Farinella ; Tao Mei ; Marco Bertini ; Hugo Jair Escalante ; Roberto Vezzani. Springer, 2021. стр. 166-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{71a6d36210cd472196ef5af56a453cad,
title = "Recognition of Tomographic Images in the Diagnosis of Stroke",
abstract = "In this paper, a method for automatic recognition of acute stroke model using non-contrast computed tomography brain images is presented. The complexity of the task lies in the fact that the dataset consists of a very small number of images. To solve the problem, we used the traditional computer vision methods and a convolutional neural network consisting of a segmentator and classifier. To increase the dataset, augmentations and sub images were used. Experiments with real CT images using validation and test samples showed that even on an extremely small dataset it is possible to train a model that will successfully cope with the classification and segmentation of images. We also proposed a way to increase the interpretability of the model.",
keywords = "Acute stroke, Classification, Convolutional neural network, Segmentation, Small dataset",
author = "Kirill Kalmutskiy and Andrey Tulupov and Vladimir Berikov",
note = "Funding Information: The work was partly supported by RFBR grant 19-29-01175. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 25th International Conference on Pattern Recognition Workshops, ICPR 2020 ; Conference date: 10-01-2021 Through 11-01-2021",
year = "2021",
doi = "10.1007/978-3-030-68821-9_16",
language = "English",
isbn = "9783030688202",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "166--171",
editor = "{Del Bimbo}, Alberto and Rita Cucchiara and Stan Sclaroff and Farinella, {Giovanni Maria} and Tao Mei and Marco Bertini and Escalante, {Hugo Jair} and Roberto Vezzani",
booktitle = "Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings",
address = "United States",

}

RIS

TY - GEN

T1 - Recognition of Tomographic Images in the Diagnosis of Stroke

AU - Kalmutskiy, Kirill

AU - Tulupov, Andrey

AU - Berikov, Vladimir

N1 - Funding Information: The work was partly supported by RFBR grant 19-29-01175. Publisher Copyright: © 2021, Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021

Y1 - 2021

N2 - In this paper, a method for automatic recognition of acute stroke model using non-contrast computed tomography brain images is presented. The complexity of the task lies in the fact that the dataset consists of a very small number of images. To solve the problem, we used the traditional computer vision methods and a convolutional neural network consisting of a segmentator and classifier. To increase the dataset, augmentations and sub images were used. Experiments with real CT images using validation and test samples showed that even on an extremely small dataset it is possible to train a model that will successfully cope with the classification and segmentation of images. We also proposed a way to increase the interpretability of the model.

AB - In this paper, a method for automatic recognition of acute stroke model using non-contrast computed tomography brain images is presented. The complexity of the task lies in the fact that the dataset consists of a very small number of images. To solve the problem, we used the traditional computer vision methods and a convolutional neural network consisting of a segmentator and classifier. To increase the dataset, augmentations and sub images were used. Experiments with real CT images using validation and test samples showed that even on an extremely small dataset it is possible to train a model that will successfully cope with the classification and segmentation of images. We also proposed a way to increase the interpretability of the model.

KW - Acute stroke

KW - Classification

KW - Convolutional neural network

KW - Segmentation

KW - Small dataset

UR - http://www.scopus.com/inward/record.url?scp=85104305649&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-68821-9_16

DO - 10.1007/978-3-030-68821-9_16

M3 - Conference contribution

AN - SCOPUS:85104305649

SN - 9783030688202

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 166

EP - 171

BT - Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings

A2 - Del Bimbo, Alberto

A2 - Cucchiara, Rita

A2 - Sclaroff, Stan

A2 - Farinella, Giovanni Maria

A2 - Mei, Tao

A2 - Bertini, Marco

A2 - Escalante, Hugo Jair

A2 - Vezzani, Roberto

PB - Springer

T2 - 25th International Conference on Pattern Recognition Workshops, ICPR 2020

Y2 - 10 January 2021 through 11 January 2021

ER -

ID: 28470427