Authors:
Andrzej Bukała
1
;
Bogusław Cyganek
1
;
2
;
Michał Koziarski
1
;
2
;
Bogdan Kwolek
1
;
2
;
Bogusław Olborski
2
;
Zbigniew Antosz
2
;
Jakub Swadźba
3
;
2
and
Piotr Sitkowski
2
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
;
3
Department of Laboratory Medicine, Andrzej Frycz Modrzewski Krakow University, Gustawa Herlinga-Grudzińskiego 1, 30-705 Kraków, Poland
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 convo
lutional 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.
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