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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 131–140 of 436 papers

TitleStatusHype
FR-MRInet: A Deep Convolutional Encoder-Decoder for Brain Tumor Segmentation with Relu-RGB and Sliding-windowCode0
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan AfricaCode0
A New Logic For Pediatric Brain Tumor SegmentationCode0
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional NetworksCode0
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
3D MRI brain tumor segmentation using autoencoder regularizationCode0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation ProblemsCode0
Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRICode0
Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing TasksCode0
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging SegmentationCode0
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