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SegAnyNeuron: a neural image segmentation network with strong generalization performance by modeling image intensity variation

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Published in:Brain Informatics
Format: Online Article RSS Article
Published: 2026
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container_title Brain Informatics
description
discipline_display Psychiatry and Neurology
discipline_facet Psychiatry and Neurology
format Online Article
RSS Article
genre Journal Article
id rss_article:58939
institution FRELIP
journal_source_facet Brain Informatics
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle SegAnyNeuron: a neural image segmentation network with strong generalization performance by modeling image intensity variation
Psychiatry and Neurology
General
Psychiatry and Neurology
sub_discipline_display General
sub_discipline_facet General
subject_display Psychiatry and Neurology
General
Psychiatry and Neurology
Psychiatry and Neurology
General
Psychiatry and Neurology
subject_facet Psychiatry and Neurology
General
Psychiatry and Neurology
title SegAnyNeuron: a neural image segmentation network with strong generalization performance by modeling image intensity variation
title_auth SegAnyNeuron: a neural image segmentation network with strong generalization performance by modeling image intensity variation
title_full SegAnyNeuron: a neural image segmentation network with strong generalization performance by modeling image intensity variation
title_fullStr SegAnyNeuron: a neural image segmentation network with strong generalization performance by modeling image intensity variation
title_full_unstemmed SegAnyNeuron: a neural image segmentation network with strong generalization performance by modeling image intensity variation
title_short SegAnyNeuron: a neural image segmentation network with strong generalization performance by modeling image intensity variation
title_sort seganyneuron: a neural image segmentation network with strong generalization performance by modeling image intensity variation
topic Psychiatry and Neurology
General
Psychiatry and Neurology
url https://link.springer.com/article/10.1186/s40708-026-00298-x