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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 276–300 of 436 papers

TitleStatusHype
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
Transfer learning for automatic brain tumor classification Using MRI Images.—0
Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation—0
TransBTS: Multimodal Brain Tumor Segmentation Using TransformerCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
Representation Disentanglement for Multi-modal brain MR AnalysisCode1
Post-hoc Overall Survival Time Prediction from Brain MRICode0
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
RFNet: Region-Aware Fusion Network for Incomplete Multi-Modal Brain Tumor SegmentationCode1
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task—0
MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architecturesCode1
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors—0
A Multi-View Dynamic Fusion Framework: How to Improve the Multimodal Brain Tumor Segmentation from Multi-Views?—0
HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation—0
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty—0
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation—0
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