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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 391–400 of 436 papers

TitleStatusHype
Brain Tumor Segmentation on MRI with Missing Modalities—0
3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRICode0
Towards annotation-efficient segmentation via image-to-image translation—0
Cascaded V-Net using ROI masks for brain tumor segmentation—0
Deep Learning with Mixed Supervision for Brain Tumor Segmentation—0
Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features—0
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data—0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—0
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS ChallengeCode0
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scansCode0
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