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

We introduce Neural Representation of Distribution (NeRD) technique, a modulefor convolutional neural networks (CNNs) that can estimate the featuredistribution by optimizing an underlying function mapping image coordinates tothe feature distribution. Using NeRD, we propose an end-to-end deep learningmodel for medical image segmentation that can compensate the negative impact offeature distribution shifting issue caused by commonly used network operationssuch as padding and pooling. An implicit function is used to represent theparameter space of the feature distribution by querying the image coordinate.With NeRD, the impact of issues such as over-segmenting and missing have beenreduced, and experimental results on the challenging white matter lesionsegmentation and left atrial segmentation verify the effectiveness of theproposed method. The code is available via https://github.com/tinymilky/NeRD.

NeRD: Neural Representation of Distribution for Medical Image Segmentation