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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 161–170 of 436 papers

TitleStatusHype
3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation—0
Detection of Under-represented Samples Using Dynamic Batch Training for Brain Tumor Segmentation from MR Images—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—0
Deep Recurrent Level Set for Segmenting Brain Tumors—0
Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation—0
Towards annotation-efficient segmentation via image-to-image translation—0
Deep Learning with Mixed Supervision for Brain Tumor Segmentation—0
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