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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 151–160 of 436 papers

TitleStatusHype
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
End-to-End Boundary Aware Networks for Medical Image Segmentation—0
Towards annotation-efficient segmentation via image-to-image translation—0
An Ensemble Approach for Brain Tumor Segmentation and Synthesis—0
Belief function-based semi-supervised learning for brain tumor segmentation—0
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation—0
3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—0
Deep Recurrent Level Set for Segmenting Brain Tumors—0
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