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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 101–110 of 436 papers

TitleStatusHype
Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)—0
Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention—0
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation—0
AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities—0
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing—0
Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation—0
PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation—0
Selective Complementary Feature Fusion and Modal Feature Compression Interaction for Brain Tumor SegmentationCode0
Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor SegmentationCode0
Clinical Inspired MRI Lesion Segmentation—0
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