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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 311–320 of 436 papers

TitleStatusHype
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint—0
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review—0
Belief function-based semi-supervised learning for brain tumor segmentation—0
Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans—0
Glioblastoma Multiforme Patient Survival Prediction—0
A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction—0
Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology—0
Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecture—0
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation—0
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task—0
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