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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

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Showing 121–130 of 436 papers

TitleStatusHype
Hybrid-Fusion Transformer for Multisequence MRICode0
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation—0
MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning—0
Whole-brain radiomics for clustered federated personalization in brain tumor segmentationCode0
Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks—0
3D-DDA: 3D Dual-Domain Attention for Brain Tumor SegmentationCode0
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology—0
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation—0
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