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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 Science and Business Media Deutschland GmbH, 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 Science and Business Media Deutschland GmbH, 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 Science and Business Media Deutschland GmbH.
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 Science and Business Media Deutschland GmbH. 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 Science and Business Media Deutschland GmbH, 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 Science and Business Media Deutschland GmbH",
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 = "Germany",
}
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 Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Pattern Recognition Workshops, ICPR 2020
Y2 - 10 January 2021 through 11 January 2021
ER -
