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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 341–350 of 436 papers

TitleStatusHype
Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI—0
A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation—0
Multi-Domain Image Completion for Random Missing Input Data—0
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional NetworksCode0
A Review on End-To-End Methods for Brain Tumor Segmentation and Overall Survival Prediction—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation ProblemsCode0
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation—0
Decentralized Differentially Private Segmentation with PATE—0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
Brain tumor segmentation with missing modalities via latent multi-source correlation representation—0
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