arXiv (OAI Expanded)
open access
On the Role of MRI Sequences in Cross-Dataset Generalization for Brain Tumor Segmentation
Brain tumor segmentation in magnetic resonance imaging (MRI) is a critical task for diagnosis and treatment planning. Despite the success of deep learning architectures such as U-Net and its variants, performance degradation across datasets remains a major challenge, particularly under domain shift
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- On the Role of MRI Sequences in Cross-Dataset Generalization for Brain Tumor Segmentation#799192
- Combining General and Domain-Specific Pretext Tasks for Brain MR Image Segmentation#1105267
- A Data-Centric Approach to Deep Learning for Brain Metastasis Analysis at MRI.#247610
- A hybrid deep learning framework based on VGG19 and U-Net for accurate brain tumor segmentation in MRI images.#362193
- Lightweight Vision Transformer-Based U-Net for Brain Tumor Segmentation from MRI#1077712
Record · ID 797769
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive