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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 251–260 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
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