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

Semi-supervised learning has attracted much attention due to its lessdependence on acquiring abundant annotations from experts compared to fullysupervised methods, which is especially important for medical imagesegmentation which typically requires intensive pixel/voxel-wise labeling bydomain experts. Although semi-supervised methods can improve the performance byutilizing unlabeled data, there are still gaps between fully supervised methodsunder extremely limited annotation scenarios. In this paper, we propose asimple yet efficient strategy to explore the usage of the Segment AnythingModel (SAM) for enhancing semi-supervised medical image segmentation.Concretely, the segmentation model trained with domain knowledge providesinformation for localization and generating input prompts to the SAM. Then thegenerated pseudo-labels of SAM are utilized as additional supervision to assistin the learning procedure of the semi-supervised framework. Experimentalresults demonstrate that SAM's assistance significantly enhances theperformance of existing semi-supervised frameworks, especially when only one ora few labeled images are available.

SemiSAM: Exploring SAM for Enhancing Semi-Supervised Medical Image Segmentation with Extremely Limited Annotations