The bag-of-features method has emerged as a useful and flexible tool that can capture medically relevant image characteristics. In this paper, we study the effect of scale and rotation invariance in the bag-of-features framework for Renal Cell Carcinoma subtype classification. We estimated the performance of different features by linear support vector machine over 10 iterations of 3-fold cross validation. For a very heterogeneous dataset labeled by an expert pathologist, we achieve a classification accuracy of 88% with four subtypes. Our study shows that rotation invariance is more important than scale invariance but combining both properties gives better classification performance. © 2011 Springer-Verlag.
CITATION STYLE
Raza, S. H., Parry, R. M., Moffitt, R. A., Young, A. N., & Wang, M. D. (2011). An analysis of scale and rotation invariance in the bag-of-features method for histopathological image classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6893 LNCS, pp. 66–74). https://doi.org/10.1007/978-3-642-23626-6_9
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