465 research outputs found
Unsupervised learning for concept detection in medical images: a comparative analysis
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
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
- …