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Brain Tumor Segmentation

Brain Tumor Segmentation is a medical image analysis task that involves the separation of brain tumors from normal brain tissue in magnetic resonance imaging (MRI) scans. The goal of brain tumor segmentation is to produce a binary or multi-class segmentation map that accurately reflects the location and extent of the tumor.

( Image credit: Brain Tumor Segmentation with Deep Neural Networks )

Papers

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Showing 76–100 of 436 papers

TitleStatusHype
Unsupervised Domain Adaptation for Pediatric Brain Tumor SegmentationCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset—0
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal DependencyCode0
Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning—0
Interactive Image Selection and Training for Brain Tumor Segmentation Network—0
Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation—0
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization—0
The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI—0
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation—0
SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform RegressionCode1
Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation—0
Patient-Specific Real-Time Segmentation in Trackerless Brain UltrasoundCode0
Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing DataCode0
On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural NetworksCode0
The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)—0
A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete ModalitiesCode0
SegFormer3D: an Efficient Transformer for 3D Medical Image SegmentationCode3
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor ClassificationCode0
LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation—0
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches—0
Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection—0
3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 ChallengeCode0
Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection—0
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