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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 251–275 of 436 papers

TitleStatusHype
Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning—0
Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy—0
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images—0
VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation—0
Within-Brain Classification for Brain Tumor Segmentation—0
Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net—0
DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling—0
3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework—0
3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation—0
3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures—0
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint—0
A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation—0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities—0
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network—0
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation—0
Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data—0
AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation—0
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation—0
A Multiscale Patch Based Convolutional Network for Brain Tumor Segmentation—0
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation—0
A Multi-View Dynamic Fusion Framework: How to Improve the Multimodal Brain Tumor Segmentation from Multi-Views?—0
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