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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
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models0
TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module ExplorationCode1
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet0
BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysisCode2
Rethinking Brain Tumor Segmentation from the Frequency Domain PerspectiveCode1
DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling0
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal EmbeddingCode0
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing ModalitiesCode0
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
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