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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 411–420 of 436 papers

TitleStatusHype
DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps SegmentationCode0
SEDNet: Shallow Encoder-Decoder Network for Brain Tumor SegmentationCode0
SegAN: Adversarial Network with Multi-scale L_1 Loss for Medical Image SegmentationCode0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation ProblemsCode0
Adaptive Active Contour Model for Brain Tumor SegmentationCode0
A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor SegmentationCode0
Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor SegmentationCode0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor SegmentationCode0
Selective Complementary Feature Fusion and Modal Feature Compression Interaction for Brain Tumor SegmentationCode0
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