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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

Showing 421430 of 436 papers

TitleStatusHype
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor SegmentationCode0
3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 ChallengeCode0
Automatic Brain Tumor Segmentation with Scale Attention NetworkCode0
Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired ImagesCode0
multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical InformationCode0
UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature FusionCode0
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival PredictionCode0
Multi-scale self-guided attention for medical image segmentationCode0
A New Logic For Pediatric Brain Tumor SegmentationCode0
Multi-step Cascaded Networks for Brain Tumor SegmentationCode0
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