期刊
ARTIFICIAL INTELLIGENCE IN MEDICINE
卷 64, 期 2, 页码 131-145出版社
ELSEVIER
DOI: 10.1016/j.artmed.2015.04.004
关键词
Digital pathology; Representation learning; Unsupervised feature learning; Basal cell carcinoma
资金
- Microsoft Research LACCIR [R1212LAC006]
- Colciencias [1225-569-34920, 0213-2013, 528/2011, 617/2013]
Objective: The paper addresses the problem of automatic detection of basal cell carcinoma (BCC) in histopathology images. In particular, it proposes a framework to both, learn the image representation in an unsupervised way and visualize discriminative features supported by the learned model. Materials and methods: This paper presents an integrated unsupervised feature learning (UFL) framework for histopathology image analysis that comprises three main stages: (1) local (patch) representation learning using different strategies (sparse autoencoders, reconstruct independent component analysis and topographic independent component analysis (TICA), (2) global (image) representation learning using a bag-of-features representation or a convolutional neural network, and (3) a visual interpretation layer to highlight the most discriminant regions detected by the model. The integrated unsupervised feature learning framework was exhaustively evaluated in a histopathology image dataset for BCC diagnosis. Results: The experimental evaluation produced a classification performance of 98.1%, in terms of the area under receiver-operating-characteristic curve, for the proposed framework outperforming by 7% the state-of-the-art discrete cosine transform patch-based representation. Conclusions: The proposed UFL-representation-based approach outperforms state-of-the-art methods for BCC detection. Thanks to its visual interpretation layer, the method is able to highlight discriminative tissue regions providing a better diagnosis support. Among the different UFL strategies tested, TICA-learned features exhibited the best performance thanks to its ability to capture low-level invariances, which are inherent to the nature of the problem. (C) 2015 Elsevier B.V. All rights reserved.
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