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Authors: Andrzej Bukała 1 ; Bogusław Cyganek 2 ; Michał Koziarski 2 ; Bogdan Kwolek 2 ; Bogusław Olborski 3 ; Zbigniew Antosz 3 ; Jakub Swadźba 4 and Piotr Sitkowski 3

Affiliations: 1 Department of Electronics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland ; 2 Diagnostyka Consilio Sp. z o.o., Ul. Kosynierów Gdyńskich 61a, 93-357 Łódź, Poland, Department of Electronics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland ; 3 Diagnostyka Consilio Sp. z o.o., Ul. Kosynierów Gdyńskich 61a, 93-357 Łódź, Poland ; 4 Diagnostyka Consilio Sp. z o.o., Ul. Kosynierów Gdyńskich 61a, 93-357 Łódź, Poland, Department of Laboratory Medicine, Andrzej Frycz Modrzewski Krakow University, Gustawa Herlinga-Grudzińskiego 1, 30-705 Kraków, Poland

ISBN: 978-989-758-402-2

ISSN: 2184-4321

Keyword(s): SIFT, Classification, Histopathology, Computer Vision, Machine Learning.

Abstract: Throughout the years, Scale-Invariant Feature Transform (SIFT) was a widely adopted method in the image matching and classification tasks. However, due to the recent advances in convolutional neural networks, the popularity of SIFT and other similar feature descriptors significantly decreased, leaving SIFT underresearched in some of the emerging applications. In this paper we examine the suitability of SIFT feature descriptors in one such task, the histopathological image classification. In the conducted experimental study we investigate the usefulness of various variants of SIFT on the BreakHis Breast Cancer Histopathological Database. While colour is known to be significant in case of human performed analysis of histopathological images, SIFT variants using different colour spaces have not been thoroughly examined on this type of data before. Observed results indicate the effectiveness of selected SIFT variants, particularly Hue-SIFT, which outperformed the reference convol utional neural network ensemble on some of the considered magnifications, simultaneously achieving lower variance. This proves the importance of using different colour spaces in classification tasks with histopathological data and shows promise to find its use in diversifying classifier ensembles. (More)

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Paper citation in several formats:
Bukała, A.; Cyganek, B.; Koziarski, M.; Kwolek, B.; Olborski, B.; Antosz, Z.; Swadźba, J. and Sitkowski, P. (2020). Classification of Histopathological Images using Scale-Invariant Feature Transform. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-402-2; ISSN 2184-4321, pages 506-512. DOI: 10.5220/0009163405060512

@conference{visapp20,
author={Andrzej Bukała. and Bogusław Cyganek. and Michał Koziarski. and Bogdan Kwolek. and Bogusław Olborski. and Zbigniew Antosz. and Jakub Swadźba. and Piotr Sitkowski.},
title={Classification of Histopathological Images using Scale-Invariant Feature Transform},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2020},
pages={506-512},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009163405060512},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Classification of Histopathological Images using Scale-Invariant Feature Transform
SN - 978-989-758-402-2
IS - 2184-4321
AU - Bukała, A.
AU - Cyganek, B.
AU - Koziarski, M.
AU - Kwolek, B.
AU - Olborski, B.
AU - Antosz, Z.
AU - Swadźba, J.
AU - Sitkowski, P.
PY - 2020
SP - 506
EP - 512
DO - 10.5220/0009163405060512

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