| DC Field | Value | Language |
|---|
| dc.contributor.author | Di Zhao | - |
| dc.contributor.author | Yi Tang | - |
| dc.contributor.author | Pertsau, D. | - |
| dc.contributor.author | Kupryianava, D. | - |
| dc.contributor.author | Gourinovitch, A. | - |
| dc.coverage.spatial | Žilina, Slovakia | en_US |
| dc.date.accessioned | 2025-11-19T09:14:20Z | - |
| dc.date.available | 2025-11-19T09:14:20Z | - |
| dc.date.issued | 2024 | - |
| dc.identifier.citation | Medical Image Segmentation with Graph Reasoning / Di Zhao, Yi Tang, D. Pertsau [et al.] // Workshop on RECI : The Third International Workshop on Reliability Engineering and Computational Intelligence : Book of Abstracts, Žilina, Slovakia, November 6-8, 2024. – Žilina, 2024. – P. 88. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/62009 | - |
| dc.description.abstract | This paper introduces a novel Synergistic Edge-Guided Graph Reasoning
Network (SEGRNet) designed to address the limitations of traditional Convolutional
Neural Networks (CNNs) in medical image segmentation, particularly in capturing
global information and modeling complex topological relationships. Existing CNNbased
methods, such as U-Net and its variants, suffer from limited receptive fields,
hindering their ability to capture comprehensive global context, especially in
structurally complex biological tissues. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | EDIS-Publishing House UNIZA, Univerzitná HB | en_US |
| dc.subject | публикации ученых | en_US |
| dc.subject | medical image segmentation | en_US |
| dc.subject | graph reasoning | en_US |
| dc.subject | graph convolutional network | en_US |
| dc.title | Medical Image Segmentation with Graph Reasoning | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Публикации в зарубежных изданиях
|
