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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 26–50 of 436 papers

TitleStatusHype
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
Inter-slice Context Residual Learning for 3D Medical Image SegmentationCode1
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric SegmentationCode1
A Joint Graph and Image Convolution Network for Automatic Brain Tumor SegmentationCode1
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
Attention U-Net: Learning Where to Look for the PancreasCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image SegmentationCode1
3D Self-Supervised Methods for Medical ImagingCode1
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image SegmentationCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
Rethinking Brain Tumor Segmentation from the Frequency Domain PerspectiveCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
GBT-SAM: Adapting a Foundational Deep Learning Model for Generalizable Brain Tumor Segmentation via Efficient Integration of Multi-Parametric MRI DataCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
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