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/

Zendy Plus

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

Zendy Tools

Zendy Open

The precise spatial and quantitative delineation of indistinct-boundarymedical objects is paramount for the accuracy of diagnostic protocols, efficacyof surgical interventions, and reliability of postoperative assessments.Despite their significance, the effective segmentation and instantaneousthree-dimensional reconstruction are significantly impeded by the paucity ofrepresentative samples in available datasets and noise artifacts. To surmountthese challenges, we introduced Stochastic Defect Injection (SDi) to augmentthe representational diversity of challenging indistinct-boundary objectswithin training corpora. Consequently, we propose the Dual-Encoder FourierGroup Harmonics Network (DEFN) to tailor noise filtration, amplify detailedfeature recognition, and bolster representation across diverse medical imagingscenarios. By incorporating Dynamic Weight Composing (DWC) loss dynamicallyadjusts model's focus based on training progression, DEFN achieves SOTAperformance on the OIMHS public dataset, showcasing effectiveness in indistinctboundary contexts. Source code for DEFN is available at:https://github.com/IMOP-lab/DEFN-pytorch.

DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object Segmentation