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

Designing deep learning algorithms for gland segmentation is crucial forautomatic cancer diagnosis and prognosis, yet the expensive annotation costhinders the development and application of this technology. In this paper, wemake a first attempt to explore a deep learning method for unsupervised glandsegmentation, where no manual annotations are required. Existing unsupervisedsemantic segmentation methods encounter a huge challenge on gland images: Theyeither over-segment a gland into many fractions or under-segment the glandregions by confusing many of them with the background. To overcome thischallenge, our key insight is to introduce an empirical cue about glandmorphology as extra knowledge to guide the segmentation process. To this end,we propose a novel Morphology-inspired method via Selective Semantic Grouping.We first leverage the empirical cue to selectively mine out proposals for glandsub-regions with variant appearances. Then, a Morphology-aware SemanticGrouping module is employed to summarize the overall information about thegland by explicitly grouping the semantics of its sub-region proposals. In thisway, the final segmentation network could learn comprehensive knowledge aboutglands and produce well-delineated, complete predictions. We conductexperiments on GlaS dataset and CRAG dataset. Our method exceeds thesecond-best counterpart over 10.56% at mIOU.

Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping