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Proceedings Paper

Hybrid image representation learning model with invariant features for basal cell carcinoma detection
Author(s): John Arevalo; Angel Cruz-Roa; Fabio A. González
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Paper Abstract

This paper presents a novel method for basal-cell carcinoma detection, which combines state-of-the-art methods for unsupervised feature learning (UFL) and bag of features (BOF) representation. BOF, which is a form of representation learning, has shown a good performance in automatic histopathology image classi cation. In BOF, patches are usually represented using descriptors such as SIFT and DCT. We propose to use UFL to learn the patch representation itself. This is accomplished by applying a topographic UFL method (T-RICA), which automatically learns visual invariance properties of color, scale and rotation from an image collection. These learned features also reveals these visual properties associated to cancerous and healthy tissues and improves carcinoma detection results by 7% with respect to traditional autoencoders, and 6% with respect to standard DCT representations obtaining in average 92% in terms of F-score and 93% of balanced accuracy.

Paper Details

Date Published: 19 November 2013
PDF: 6 pages
Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 89220M (19 November 2013); doi: 10.1117/12.2035530
Show Author Affiliations
John Arevalo, Univ. Nacional de Colombia (Colombia)
Angel Cruz-Roa, Univ. Nacional de Colombia (Colombia)
Fabio A. González, Univ. Nacional de Colombia (Colombia)


Published in SPIE Proceedings Vol. 8922:
IX International Seminar on Medical Information Processing and Analysis
Jorge Brieva; Boris Escalante-Ramírez, Editor(s)

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