A deep ensemble learning framework for glioma segmentation and grading prediction
Abstract The segmentation and risk grade prediction of gliomas based on preoperative multimodal magnetic resonance imaging (MRI) are crucial tasks in computer-aided diagnosis. Due to the significant heterogeneity between and within tumors, existing methods mainly rely on single-task approaches, over...
Saved in:
Main Authors: | Liang Wen, Hui Sun, Guobiao Liang, Yue Yu |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-02-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-025-87127-z |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy
by: Qi Wan, et al.
Published: (2025-01-01) -
Cinnamaldehyde impacts key cellular signaling pathways for induction of programmed cell death in high-grade and low-grade human glioma cells
by: Yoo Na Kim, et al.
Published: (2025-01-01) -
Detection and Segmentation of Glioma Tumors Using an Improved Visual Geometry Group (IVGG) Deep Learning Structure
by: Parameswari Alagarsamy, et al.
Published: (2025-02-01) -
Attention-enhanced optimized deep ensemble network for effective facial emotion recognition
by: Taimoor Khan, et al.
Published: (2025-04-01) -
Differentiation therapy targeting the stalled epigenetic developmental programs in pediatric high-grade gliomas
by: Wang Xiang, et al.
Published: (2025-02-01)