4 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
Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare
Content-Based Image Retrieval (CBIR) locates, retrieves and displays images
alike to one given as a query, using a set of features. It demands accessible
data in medical archives and from medical equipment, to infer meaning after
some processing. A problem similar in some sense to the target image can aid
clinicians. CBIR complements text-based retrieval and improves evidence-based
diagnosis, administration, teaching, and research in healthcare. It facilitates
visual/automatic diagnosis and decision-making in real-time remote
consultation/screening, store-and-forward tests, home care assistance and
overall patient surveillance. Metrics help comparing visual data and improve
diagnostic. Specially designed architectures can benefit from the application
scenario. CBIR use calls for file storage standardization, querying procedures,
efficient image transmission, realistic databases, global availability, access
simplicity, and Internet-based structures. This chapter recommends important
and complex aspects required to handle visual content in healthcare.Comment: 28 pages, 6 figures, Book Chapter from "Encyclopedia of E-Health and
Telemedicine
Image Annotation and Topic Extraction Using Super-Word Latent Dirichlet
This research presents a multi-domain solution that uses text and images to iteratively improve automated information extraction. Stage I uses local text surrounding an embedded image to provide clues that help rank-order possible image annotations. These annotations are forwarded to Stage II, where the image annotations from Stage I are used as highly-relevant super-words to improve extraction of topics. The model probabilities from the super-words in Stage II are forwarded to Stage III where they are used to refine the automated image annotation developed in Stage I. All stages demonstrate improvement over existing equivalent algorithms in the literature