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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 76–100 of 436 papers

TitleStatusHype
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restorationCode1
3D Self-Supervised Methods for Medical ImagingCode1
Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance ImagingCode1
DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR ImagesCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated FusionCode1
Knowledge Distillation for Brain Tumor SegmentationCode1
Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIsCode1
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty AnalysisCode1
Models Genesis: Generic Autodidactic Models for 3D Medical Image AnalysisCode1
Lesion Focused Super-ResolutionCode1
Attention U-Net: Learning Where to Look for the PancreasCode1
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
Brain Tumor Segmentation with Deep Neural NetworksCode1
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models—0
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet—0
DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling—0
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal EmbeddingCode0
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing ModalitiesCode0
VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation—0
UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature FusionCode0
Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI—0
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