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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 351–360 of 436 papers

TitleStatusHype
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings—0
Deepfake Image Generation for Improved Brain Tumor Segmentation—0
Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection—0
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation—0
Deep Learning with Mixed Supervision for Brain Tumor Segmentation—0
Deep Recurrent Level Set for Segmenting Brain Tumors—0
Detection of Under-represented Samples Using Dynamic Batch Training for Brain Tumor Segmentation from MR Images—0
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation—0
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