XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision Xlstm and Heteromodal Variational Encoder-Decoder

Neurogliomas are among the most aggressive forms of can-cer, presenting considerable challenges in both treatment and monitoring due to their unpredictable biological behav-ior. Magnetic resonance imaging (MRI) is currently the preferred method for diagnosing and monitoring gliomas. However, the lac...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Proceedings (International Symposium on Biomedical Imaging) S. 1 - 5
Hauptverfasser:	Zhu, Shenghao, Chen, Yifei, Jiang, Shuo, Chen, Weihong, Liu, Chang, Wang, Yuanhan, Chen, Xu, Ke, Yifan, Qin, Feiwei, Wang, Changmiao, Zhu, Zhu
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 14.04.2025
Schlagworte:	Accuracy Biological system modeling Brain modeling Brain Tumor Segmentation Brain tumors Image reconstruction Image segmentation Magnetic resonance imaging Missing Modality Monitoring Multi-task Learning Multimodal MRI Multitasking Neuroglioma Technological innovation
ISSN:	1945-8452
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!