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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 301–310 of 436 papers

TitleStatusHype
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks—0
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation—0
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—0
Belief function-based semi-supervised learning for brain tumor segmentation—0
Towards annotation-efficient segmentation via image-to-image translation—0
End-to-End Boundary Aware Networks for Medical Image Segmentation—0
Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand—0
Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation—0
Brain Tumor Detection Based On Mathematical Analysis and Symmetry Information—0
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