Szczegóły publikacji
Opis bibliograficzny
Rodecon-net: medical image segmentation via robust decoupling and contrast-enhanced fusion / Yongquan Xue, Zhaoru Guo, Zhaozhao Su, Chong Peng, Jun Feng, Pan Zhou, Marcin PIETROŃ, Xiyuan Wang, Liejun Wang, Panpan Zheng // W: MM'25 [Dokument elektroniczny] : proceedings of the 33rd ACM international conference on Multimedia : October 27–31, 2025, Dublin, Ireland. — Wersja do Windows. — Dane tekstowe. — New York : Association for Computing Machinery, 2025. — e-ISBN: 979-8-4007-2035-2. — S. 4427-4435. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 4435, Abstr. — Publikacja dostępna online od: 2025-10-27
Autorzy (10)
- Xue Yongquan
- Guo Zhanhu
- Su Zhaozhao
- Peng Chong
- Feng Jun
- Zhou Pan
- AGHPietroń Marcin
- Wang Xiyuan
- Wang Liejun
- Zheng Panpan
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 166236 |
|---|---|
| Data dodania do BaDAP | 2026-03-13 |
| Tekst źródłowy | URL |
| DOI | 10.1145/3746027.3755440 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | ACM Multimedia 2025 |
Abstract
Medical image segmentation is crucial for clinical decision-making, treatment planning, and disease tracking. Nonetheless, it confronts two significant challenges: the presence of ”soft boundaries” between the foreground and background exacerbated by poor illumination and low contrast, and the misleading co-occurrence of salient and non-salient objects during the training phase, which complicates the model's accuracy in distinguishing relevant features. To overcome these challenges, we introduce RoDeCon-Net, a novel framework engineered to enhance medical image segmentation. RoDeCon-Net incorporates a Feature Decoupling Unit (FDU) that dynamically separates encoded features into foreground, background, and uncertain regions, using advanced attention mechanisms to refine feature distinction and reduce uncertainty. Additionally, our Contrast-driven Feature Alignment Unit (CFAU) and Cross-layer Feature Cascade Unit (CFCU) synergize to reinforce feature contrasts and promote effective multi-level feature fusion, thus improving the detection of salient objects amidst complex backgrounds and handling various object scales within images. Comprehensive evaluations of RoDeCon-Net on five diverse medical image datasets validate its superior performance and versatility, showcasing its potential to set new benchmarks in medical image segmentation. Our code is available on https://github.com/ILoveACM-MM/RoDeCon-Net.
