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

Understanding of spatial attributes is central to effective 3D radiologyimage analysis where crop-based learning is the de facto standard. Given animage patch, its core spatial properties (e.g., position & orientation) providehelpful priors on expected object sizes, appearances, and structures throughinherent anatomical consistencies. Spatial correspondences, in particular, caneffectively gauge semantic similarities between inter-image regions, whiletheir approximate extraction requires no annotations or overbearingcomputational costs. However, recent 3D contrastive learning approaches eitherneglect correspondences or fail to maximally capitalize on them. To this end,we propose an extensible 3D contrastive framework (Spade, for SpatialDebiasing) that leverages extracted correspondences to select more effectivepositive & negative samples for representation learning. Our method learns bothglobally invariant and locally equivariant representations with downstreamsegmentation in mind. We also propose separate selection strategies for global& local scopes that tailor to their respective representational requirements.Compared to recent state-of-the-art approaches, Spade shows notableimprovements on three downstream segmentation tasks (CT Abdominal Organ, CTHeart, MR Heart).

Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images