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19 November 2013 Hybrid image representation learning model with invariant features for basal cell carcinoma detection
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Proceedings Volume 8922, IX International Seminar on Medical Information Processing and Analysis; 89220M (2013) https://doi.org/10.1117/12.2035530
Event: IX International Seminar on Medical Information Processing and Analysis, 2013, Mexico City, Mexico
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.
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John Arevalo, Angel Cruz-Roa, and Fabio A. González "Hybrid image representation learning model with invariant features for basal cell carcinoma detection", Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 89220M (19 November 2013); https://doi.org/10.1117/12.2035530
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