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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 51–75 of 436 papers

TitleStatusHype
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 ChallengeCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
A Joint Graph and Image Convolution Network for Automatic Brain Tumor SegmentationCode1
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
Medical Image Segmentation Using Squeeze-and-Expansion TransformersCode1
The Federated Tumor Segmentation (FeTS) ChallengeCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
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
RFNet: Region-Aware Fusion Network for Incomplete Multi-Modal Brain Tumor SegmentationCode1
MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architecturesCode1
Inter-slice Context Residual Learning for 3D Medical Image SegmentationCode1
DR-Unet104 for Multimodal MRI brain tumor segmentationCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challengeCode1
nnU-Net for Brain Tumor SegmentationCode1
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solutionCode1
What is the best data augmentation for 3D brain tumor segmentation?Code1
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric SegmentationCode1
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
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