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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 61–70 of 436 papers

TitleStatusHype
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance ImagingCode0
Fed-MUnet: Multi-modal Federated Unet for Brain Tumor SegmentationCode1
Leveraging SeNet and ResNet Synergy within an Encoder-Decoder Architecture for Glioma Detection—0
Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection—0
Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities—0
Detection of Under-represented Samples Using Dynamic Batch Training for Brain Tumor Segmentation from MR Images—0
MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment—0
Decoupling Feature Representations of Ego and Other Modalities for Incomplete Multi-modal Brain Tumor SegmentationCode0
A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor SegmentationCode0
UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor SegmentationCode0
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