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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 51–100 of 436 papers

TitleStatusHype
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic ClassificationCode1
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solutionCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
What is the best data augmentation for 3D brain tumor segmentation?Code1
A Joint Graph and Image Convolution Network for Automatic Brain Tumor SegmentationCode1
Single MR Image Super-Resolution using Generative Adversarial NetworkCode1
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image SegmentationCode1
Attention U-Net: Learning Where to Look for the PancreasCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR ImagesCode1
DR-Unet104 for Multimodal MRI brain tumor segmentationCode1
E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 ChallengeCode1
Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decompositionCode1
Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRICode1
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor SegmentationCode1
Fed-MUnet: Multi-modal Federated Unet for Brain Tumor SegmentationCode1
Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challengeCode1
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challengeCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
Brain Tumor Segmentation with Deep Neural NetworksCode1
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restorationCode1
Lesion Focused Super-ResolutionCode1
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric SegmentationCode1
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor SegmentationCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
3D Self-Supervised Methods for Medical ImagingCode1
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis TasksCode1
Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance ImagingCode1
nnU-Net for Brain Tumor SegmentationCode1
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image AnalysisCode1
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction—0
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation—0
A Structural Graph-Based Method for MRI Analysis—0
3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures—0
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
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