  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 the 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 space evaluated using images
from the ImageCLEF 2017 concept detection task. We conclude that it is possible
to obtain more powerful representations with modern deep learning approaches,
in contrast with previously popular computer vision methods. Although
generative adversarial networks can provide good results, they are harder to
succeed in highly varied data sets. The possibility of semi-supervised
learning, as well as their use in medical information retrieval problems, are
the next steps to be strongly considered.
