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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 371–380 of 436 papers

TitleStatusHype
End-to-End Boundary Aware Networks for Medical Image Segmentation—0
Models Genesis: Generic Autodidactic Models for 3D Medical Image AnalysisCode1
Multi-step Cascaded Networks for Brain Tumor SegmentationCode0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—0
A Structural Graph-Based Method for MRI Analysis—0
Robustifying deep networks for image segmentation—0
Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation—0
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation—0
Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired ImagesCode0
Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior—0
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