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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

Showing 426436 of 436 papers

TitleStatusHype
UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature FusionCode0
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival PredictionCode0
Multi-scale self-guided attention for medical image segmentationCode0
A New Logic For Pediatric Brain Tumor SegmentationCode0
Multi-step Cascaded Networks for Brain Tumor SegmentationCode0
A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete ModalitiesCode0
Semi-Supervised Variational Autoencoder for Survival PredictionCode0
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image SegmentationCode0
Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessmentCode0
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image SegmentationCode0
Decoupling Feature Representations of Ego and Other Modalities for Incomplete Multi-modal Brain Tumor SegmentationCode0
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