An Evidential Mask Transformer for Left Atrium Segmentation

dc.contributor.authorUslu, Fatmatulzehra
dc.date.accessioned2026-02-08T15:08:15Z
dc.date.available2026-02-08T15:08:15Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThe segmentation of the left atrium (LA) is required to calculate the clinical parameters of the LA, to identify diseases related to its remodeling. Generally, convolutional networks have been used for this task. However, their performance may be limited as a result of the use of local convolution operations for feature extraction. Also, such models usually need extra steps to provide uncertainty maps such as multiple forward passes for Monte Carlo dropouts or training multiple models for ensemble learning. To address these issues, we adapt mask transformers for LA segmentation which effectively use both local and global information, and train them with evidential learning to generate uncertainty maps from the learned Dirichlet distribution, with a single forward pass. We validated our approach on the STACOM 2013 dataset and found that our method can produce better segmentation performance than baseline models, and can identify locations our model’s responses are not trustable.
dc.identifier.doi10.21605/cukurovaumfd.1560046
dc.identifier.endpage646
dc.identifier.issn2757-9255
dc.identifier.issue3
dc.identifier.startpage639
dc.identifier.trdizinid1269792
dc.identifier.urihttps://doi.org/10.21605/cukurovaumfd.1560046
dc.identifier.urihttps://hdl.handle.net/20.500.12885/4879
dc.identifier.volume39
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofÇukurova Üniversitesi Mühendislik Fakültesi dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR-Dizin_20260207
dc.subjectUncertainty
dc.subjectImage segmentation
dc.subjectMRI images
dc.subjectMask transformer
dc.subjectEvidential learning
dc.titleAn Evidential Mask Transformer for Left Atrium Segmentation
dc.typeArticle

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