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Unsupervised Learning for Concept Detection in Medical Images: A Comparative Analysis

Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro; Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
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Appl. Sci. 2018, 8(8), 1213; https://doi.org/10.3390/app8081213
Received: 18 June 2018 / Revised: 13 July 2018 / Accepted: 21 July 2018 / Published: 24 July 2018
(This article belongs to the Special Issue Deep Learning and Big Data in Healthcare)
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Abstract

As digital medical imaging becomes more prevalent and archives increase in size, representation learning exposes an interesting opportunity for enhanced medical decision support systems. On the other hand, medical imaging data is often scarce and short on annotations. In this paper, we present an assessment of unsupervised feature learning approaches for images in biomedical literature which can be applied to automatic biomedical concept detection. Six unsupervised representation learning methods were built, including traditional bags of visual words, autoencoders, and generative adversarial networks. Each model was trained, and their respective feature spaces evaluated using images from the ImageCLEF 2017 concept detection task. The highest mean F1 score of 0.108 was obtained using representations from an adversarial autoencoder, which increased to 0.111 when combined with the representations from the sparse denoising autoencoder. We conclude that it is possible to obtain more powerful representations with modern deep learning approaches than with previously popular computer vision methods. The possibility of semi-supervised learning as well as its use in medical information retrieval problems are the next steps to be strongly considered. View Full-Text
Keywords: representation learning; unsupervised learning; deep learning; content-based image retrieval representation learning; unsupervised learning; deep learning; content-based image retrieval
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Pinho, E.; Costa, C. Unsupervised Learning for Concept Detection in Medical Images: A Comparative Analysis. Appl. Sci. 2018, 8, 1213.

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