Papers Brain Tumor Segmentation
“Brain Tumor Segmentation” 태그가 달린 논문 525편 · 필터 해제
GSToken: Geometry-Structured Gaussian Tokens for Compact 3D Medical Image Representation
Effective segmentation of multi-modal MRI is central to improving neural network accuracy in brain tumor recognition. Existing methods typically compress 3D volumes into token sequences via fixed patch encoding or learne…
Brain Tumor SegmentationSimilarity Weighted Aggregation with Global Differential Privacy for Federated Brain Lesion Segmentation
Federated Learning (FL) enables collaborative training of machine learning models across multiple institutions without sharing sensitive data, making it particularly suitable for medical imaging applications. However, he…
Brain Tumor SegmentationLesion SegmentationFederated LearningMIND: Multimodal Intent-Driven Network via Diffusion Transformers for Medical Image Fusion
Medical image fusion aims to integrate complementary information from diverse imaging modalities to support clinical diagnosis. Existing methods typically apply uniform fusion rules globally, lacking a deep understanding…
Brain Tumor SegmentationPartial Information Decomposition as a Multi-Contrast 3D MRI Selection Strategy for Resource-Constrained Deep Neural Network Training in Brain Tumor Segmentation
Multi-contrast 3D MRI segmentation can be computationally demanding when all available sequences are used. We evaluate a pre-training Partial Information Decomposition framework that ranks input pairs according to their …
Brain Tumor SegmentationRUFNet: Query-Guided Support Mask Refinement and Uncertainty Fusion based on Hybrid Mamba for Few-Shot Brain Tumor Segmentation
Few-shot brain tumor segmentation remains challenging due to noisy support masks, inter-patient variations between support and query images, and the lack of pixel-wise confidence estimation. This study proposes RUFNet, a…
Medical Image SegmentationBrain Tumor SegmentationSet-Inclusive Uncertainty Modeling for Robust Brain Tumor Segmentation
Multimodal MRI is essential for accurate brain tumor segmentation. However, acquiring all modalities at inference is often challenging in practice, which causes intrinsic uncertainty due to unavoidable information loss. …
Brain Tumor SegmentationFrom Convolution to Transformer: A Comparative Study of U-Net Variants for Brain Tumor and Retinal Vessel Segmentation
Medical image segmentation plays an important role in computer aided diagnosis, treatment planning, and disease monitoring. U-Net has been widely used for biomedical image segmentation because of its encoder decoder stru…
Retinal Vessel SegmentationMedical Image SegmentationBrain Tumor SegmentationCoMNet: A MedNeXt-CorrDiff Framework for Multi-Site Brain Tumor Segmentation
Accurate brain tumor segmentation from multiparametric magnetic resonance imaging (MRI) is critical for treatment planning, response assessment, and neuro-oncology research. However, automated segmentation remains a diff…
Brain Tumor SegmentationDiffusion-Refined Segmentation and Vision-Language Interpretation for Pediatric Brain Tumor MRI
Accurate pediatric brain tumor segmentation remains challenging due to limited annotated data, heterogeneous imaging phenotypes, diffuse tumor boundaries, and class imbalance across tumor subregions. Here, we present a t…
Brain Tumor SegmentationA Novel Global Context-aware Deep Neural Network for Enhanced Brain Tumor Segmentation using Magnetic Resonance Images
Brain cancer's severity necessitates precise brain tumor segmentation, which is crucial for effective brain tumor diagnosis. Manual identification, burdened by high costs, labor, and error risks, highlights the need for …
Brain Tumor SegmentationNot All NVFP4 QAT Recipes Are Equal: How Architecture and Scale Shape Model Quality for Anomaly Segmentation
Real-time anomaly segmentation demands both high recall and efficient low-precision inference. We study the three-way interaction of model architecture, model scale, and FP4 quantization-aware training (QAT) recipe on a …
Brain Tumor SegmentationD3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities
Accurate brain tumor segmentation using multi-parametric MRI is critical for effective treatment planning. However, in clinical settings, complete acquisition of all MRI sequences is not always possible. The absence of c…
Brain Tumor SegmentationSegGuidedNet: Sub-Region-Aware Attention Supervision for Interpretable Brain Tumor Segmentation
Accurate segmentation of brain tumour sub-regions from multi-parametric MRI is critical for treatment planning yet remains challenging due to morphological variability, class imbalance, and overlapping appearances of tum…
Brain Tumor SegmentationVirtual Nodes Guided Dynamic Graph Neural Network for Brain Tumor Segmentation with Missing Modalities
Multimodal magnetic resonance imaging (MRI) is crucial for brain tumor segmentation, with many methods leveraging its four key modalities to capture complementary information for effective sub-region analysis. However, t…
Brain Tumor SegmentationGraph Neural NetworkDegradation-Aware Blur-Segmentation of Brain Tumor
Multimodal 3D MRI brain tumor segmentation is a pivotal step in radiotherapy target delineation, surgical planning and post-treatment assessment. Existing methods often assume artifact-free MRI images. However, inevitabl…
Brain Tumor SegmentationMHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentation
Brain tumors exhibit high heterogeneity in morphology and multimodal contrast, making manual slice-by-slice de lineation time-consuming and experience-dependent, thus necessitating efficient and stable automated segmenta…
Brain Tumor SegmentationMedFL-Stress: A Systematic Robustness Evaluation of Federated Brain Tumor Segmentation under Cross-Hospital MRI Appearance Shift
Federated learning enables hospitals to collaboratively train segmentation models without sharing patient data. However, current evaluation protocols report only average performance across clients, masking failures at in…
Brain Tumor SegmentationFederated LearningDALight-3D: A Lightweight 3D U-Net for Brain Tumor Segmentation from Multi-Modal MRI
Automatic brain tumor segmentation from multi-modal MRI remains challenging because volumetric models often incur substantial computational cost. This paper presents DALight-3D, a compact 3D U-Net variant that combines d…
Brain Tumor SegmentationEnhanced 3D Brain Tumor Segmentation Using Assorted Precision Training
A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of non-essential cells close to or throughout the brain. Symptoms of this ailment include headaches, …
Brain Tumor SegmentationInfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction
Gliomas are aggressive brain tumors that infiltrate surrounding tissue beyond the visible tumor margins observed on Magnetic Resonance Imaging (MRI). Predicting the spatial extent of this infiltration is essential for su…
Brain Tumor Segmentation