ArticleScientific reports2025
A deep ensemble learning framework for glioma segmentation and grading prediction.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Impact of MRI modality selection on glioma sub-region segmentation using 3D U-Net and Attention U-Net: A comparative evaluation across ten MRI sequences.European journal of radiology open · 2026Article
- Deep Learning for Automated Detection and Segmentation of Meningioma on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- A Review of U-Net Based Deep Learning Frameworks for MRI-Based Brain Tumor Segmentation.Diagnostics (Basel, Switzerland) · 2026Review
- A Sensor-Oriented Multimodal Medical Data Acquisition and Modeling Framework for Tumor Grading and Treatment Response Analysis.Sensors (Basel, Switzerland) · 2026Article
- Externally validated explainable 3D CNN ensemble model for non-invasive prediction of IDH and MGMT status in gliomas.Frontiers in oncology · 2026Article
- Article
- Automated Brain Tumor MRI Segmentation Using ARU-Net with Residual-Attention Modules.Diagnostics (Basel, Switzerland) · 2025Article
- A Review on Deep Learning Methods for Glioma Segmentation, Limitations, and Future Perspectives.Journal of imaging · 2025Review
- GlioMODA: Robust glioma segmentation in clinical routine.Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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, overlooking the inherent correlation between segmentation and grading tasks. Furthermore, the limited availability of glioma grading data presents further challenges. To address these issues, we propose a deep-ensemble learning framework based on multimodal MRI and the U-Net model, which simultaneously performs glioma segmentation and risk grade prediction. We introduce asymmetric convolution and dual-domain attention in the encoder, fully integrating effective information from different modalities, enhancing the extraction of features from critical regions, and constructing a dual-branch decoder that combines spatial features and global semantic information for both segmentation and grading. In addition, we propose a weighted composite adaptive loss function to balance the optimization objectives of the two tasks. Our experimental results on the BraTS dataset demonstrate that our method outperforms state-of-the-art methods, yielding superior segmentation accuracy and precise risk grade prediction.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.