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Proceedings Paper

A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation
Author(s): Angel Cruz-Roa; John Arevalo; Ajay Basavanhally; Anant Madabhushi; Fabio González
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Paper Abstract

Learning data representations directly from the data itself is an approach that has shown great success in different pattern recognition problems, outperforming state-of-the-art feature extraction schemes for different tasks in computer vision, speech recognition and natural language processing. Representation learning applies unsupervised and supervised machine learning methods to large amounts of data to find building-blocks that better represent the information in it. Digitized histopathology images represents a very good testbed for representation learning since it involves large amounts of high complex, visual data. This paper presents a comparative evaluation of different supervised and unsupervised representation learning architectures to specifically address open questions on what type of learning architectures (deep or shallow), type of learning (unsupervised or supervised) is optimal. In this paper we limit ourselves to addressing these questions in the context of distinguishing between anaplastic and non-anaplastic medulloblastomas from routine haematoxylin and eosin stained images. The unsupervised approaches evaluated were sparse autoencoders and topographic reconstruct independent component analysis, and the supervised approach was convolutional neural networks. Experimental results show that shallow architectures with more neurons are better than deeper architectures without taking into account local space invariances and that topographic constraints provide useful invariant features in scale and rotations for efficient tumor differentiation.

Paper Details

Date Published: 28 January 2015
PDF: 6 pages
Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 92870G (28 January 2015); doi: 10.1117/12.2073849
Show Author Affiliations
Angel Cruz-Roa, Univ. Nacional de Colombia (Colombia)
John Arevalo, Univ. Nacional de Colombia (Colombia)
Ajay Basavanhally, Case Western Reserve Univ. (United States)
Anant Madabhushi, Case Western Reserve Univ. (United States)
Fabio González, Univ. Nacional de Colombia (Colombia)


Published in SPIE Proceedings Vol. 9287:
10th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore, Editor(s)

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