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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 401–410 of 436 papers

TitleStatusHype
Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty PredictionCode0
M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient SupervisionCode0
Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?Code0
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal EmbeddingCode0
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival PredictionCode0
Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma SegmentationCode0
Brain Tumor Detection using Convolutional Neural NetworkCode0
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance ImagingCode0
Volumetric medical image segmentation through dual self-distillation in U-shaped networksCode0
Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation ProblemCode0
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