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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 110 of 436 papers

TitleStatusHype
SegFormer3D: an Efficient Transformer for 3D Medical Image SegmentationCode3
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image SegmentationCode3
BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysisCode2
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)Code2
TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical ImagesCode2
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI ImagesCode2
TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module ExplorationCode1
Rethinking Brain Tumor Segmentation from the Frequency Domain PerspectiveCode1
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
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