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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 251–300 of 436 papers

TitleStatusHype
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout—0
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
A Joint Graph and Image Convolution Network for Automatic Brain Tumor SegmentationCode1
Self-supervised Tumor Segmentation through Layer Decomposition—0
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation—0
Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation—0
3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework—0
MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification—0
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation—0
Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma SegmentationCode0
AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor SegmentationCode0
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic ClassificationCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
HMM Model for Brain Tumor Detection and ClassificationCode0
Knowledge distillation from multi-modal to mono-modal segmentation networks—0
Conditional generator and multi-sourcecorrelation guided brain tumor segmentation with missing MR modalities—0
Experimenting with Knowledge Distillation techniques for performing Brain Tumor Segmentation—0
Brain tumour segmentation using a triplanar ensemble of U-NetsCode0
Medical Image Segmentation Using Squeeze-and-Expansion TransformersCode1
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain AdaptationCode0
The Federated Tumor Segmentation (FeTS) ChallengeCode1
Medical Transformer: Universal Brain Encoder for 3D MRI Analysis—0
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation—0
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities—0
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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