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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 7180 of 436 papers

TitleStatusHype
Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIsCode0
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 20230
Brain Tumor Segmentation in MRI Images with 3D U-Net and Contextual Transformer0
SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text CuesCode1
RobU-Net: a heuristic robust multi-class brain tumor segmentation approaches for MRI scans0
Unsupervised Domain Adaptation for Pediatric Brain Tumor SegmentationCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset0
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal DependencyCode0
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