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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 376–400 of 436 papers

TitleStatusHype
Robustifying deep networks for image segmentation—0
Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation—0
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation—0
Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired ImagesCode0
Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior—0
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmCode0
Multi-scale self-guided attention for medical image segmentationCode0
One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor SegmentationCode0
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation—0
Brain Tumor Detection using Convolutional Neural NetworkCode0
Fully Automatic Brain Tumor Segmentation using a Normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow—0
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
Consistent estimation of the max-flow problem: Towards unsupervised image segmentation—0
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation—0
3D MRI brain tumor segmentation using autoencoder regularizationCode0
Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function—0
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