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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 111–120 of 436 papers

TitleStatusHype
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation—0
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings—0
Position Paper: Building Trust in Synthetic Data for Clinical AI—0
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation—0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan AfricaCode0
Incomplete Multi-modal Brain Tumor Segmentation via Learnable Sorting State Space Model—0
KMD: Koopman Multi-modality Decomposition for Generalized Brain Tumor Segmentation under Incomplete Modalities—0
SuperLightNet: Lightweight Parameter Aggregation Network for Multimodal Brain Tumor SegmentationCode0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
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