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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 8190 of 436 papers

TitleStatusHype
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric SegmentationCode1
Knowledge Distillation for Brain Tumor SegmentationCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
Representation Disentanglement for Multi-modal brain MR AnalysisCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
3D Self-Supervised Methods for Medical ImagingCode1
Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor SegmentationCode1
NestedFormer: Nested Modality-Aware Transformer for Brain Tumor SegmentationCode1
What is the best data augmentation for 3D brain tumor segmentation?Code1
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