Combining Unsupervised Feature Learning and Riesz Wavelets for Histopathology Image Representation: Application to Identifying Anaplastic Medulloblastoma
S. Otálora, A. Cruz-Roa, J. Arevalo, M. Atzori, A. Madabhushi, A.R. Judkins, F. González, H. Müller, A. Depeursinge
Proceedings of the Eighteenth International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'15), Münich, Federal Republic of Germany, October 5-9, 2015, [Lecture Notes in Computer Science, vol. 9349, Springer, 2015], pp. 581–588.
Medulloblastoma (MB) is a type of brain cancer that represent roughly 25% of all brain tumors in children. In the anaplastic medulloblastoma subtype, it is important to identify the degree of irregularity and lack of organizations of cells as this correlates to disease aggressiveness and is of clinical value when evaluating patient prognosis. This paper presents an image representation to distinguish these subtypes in histopathology slides. The approach combines learned features from (i) an unsupervised feature learning method using topographic independent component analysis that captures scale, color and translation invariances, and (ii) learned linear combinations of Riesz wavelets calculated at several orders and scales capturing the granularity of multiscale rotation-covariant information. The contribution of this work is to show that the combination of two complementary approaches for feature learning (unsupervised and supervised) improves the classification performance. Our approach outperforms the best methods in literature with statistical significance, achieving 99% accuracy over region-based data comprising 7,500 square regions from 10 patient studies diagnosed with medulloblastoma (5 anaplastic and 5 non-anaplastic).
@INPROCEEDINGS(http://bigwww.epfl.ch/publications/otalora1501.html,
AUTHOR="Ot{\'{a}}lora, S. and Cruz-Roa, A. and Arevalo, J. and Atzori,
M. and Madabhushi, A. and Judkins, A.R. and Gonz{\'{a}}lez, F. and
M{\"{u}}ller, H. and Depeursinge, A.",
TITLE="Combining Unsupervised Feature Learning and {R}iesz Wavelets for
Histopathology Image Representation: {A}pplication to Identifying
Anaplastic Medulloblastoma",
BOOKTITLE="Proceedings of the Eighteenth International Conference on
Medical Image Computing and Computer Assisted Intervention
({MICCAI'15})",
YEAR="2015",
editor="",
volume="9349",
series="Lecture Notes in Computer Science",
pages="581--588",
address="M{\"{u}}nich, Federal Republic of Germany",
month="October 5-9,",
organization="",
publisher="Springer",
note="")