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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deformable image registration plays a critical role in various tasks ofmedical image analysis. A successful registration algorithm, either derivedfrom conventional energy optimization or deep networks requires tremendousefforts from computer experts to well design registration energy or tocarefully tune network architectures for the specific type of medical data. Totackle the aforementioned problems, this paper proposes an automated learningregistration algorithm (AutoReg) that cooperatively optimizes botharchitectures and their corresponding training objectives, enablingnon-computer experts, e.g., medical/clinical users, to conveniently findoff-the-shelf registration algorithms for diverse scenarios. Specifically, weestablish a triple-level framework to deduce registration network architecturesand objectives with an auto-searching mechanism and cooperating optimization.We conduct image registration experiments on multi-site volume datasets andvarious registration tasks. Extensive results demonstrate that our AutoReg mayautomatically learn an optimal deep registration network for given volumes andachieve state-of-the-art performance, also significantly improving computationefficiency than the mainstream UNet architectures (from 0.558 to 0.270 secondsfor a 3D image pair on the same configuration).

Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions