English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Content-based image retrieval (CBIR) of medical images is a crucial task thatcan contribute to a more reliable diagnosis if applied to big data. Recentadvances in feature extraction and classification have enormously improved CBIRresults for digital images. However, considering the increasing accessibilityof big data in medical imaging, we are still in need of reducing both memoryrequirements and computational expenses of image retrieval systems. This workproposes to exclude the features of image blocks that exhibit a low encodingerror when learned by a $n/p/n$ autoencoder ($p\!<\!n$). We examine thehistogram of autoendcoding errors of image blocks for each image class tofacilitate the decision which image regions, or roughly what percentage of animage perhaps, shall be declared relevant for the retrieval task. This leads toreduction of feature dimensionality and speeds up the retrieval process. Tovalidate the proposed scheme, we employ local binary patterns (LBP) and supportvector machines (SVM) which are both well-established approaches in CBIRresearch community. As well, we use IRMA dataset with 14,410 x-ray images astest data. The results show that the dimensionality of annotated featurevectors can be reduced by up to 50% resulting in speedups greater than 27% atexpense of less than 1% decrease in the accuracy of retrieval when validatingthe precision and recall of the top 20 hits.

Autoencoding the Retrieval Relevance of Medical Images