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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 411–420 of 436 papers

TitleStatusHype
Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRICode0
Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks—0
Autofocus Layer for Semantic SegmentationCode0
Attention U-Net: Learning Where to Look for the PancreasCode1
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice LossCode0
Segmenting Brain Tumors with Symmetry—0
Automated Tumor Segmentation and Brain Mapping for the Tumor Area—0
A Multiscale Patch Based Convolutional Network for Brain Tumor Segmentation—0
Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation—0
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