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Tumor Segmentation

Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.

Papers

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Showing 761–770 of 786 papers

TitleStatusHype
DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps SegmentationCode0
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival PredictionCode0
Neural Gas Network Image Features and Segmentation for Brain Tumor Detection Using Magnetic Resonance Imaging DataCode0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image SegmentationCode0
A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor SegmentationCode0
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
Cross-modal tumor segmentation using generative blending augmentation and self trainingCode0
3D MRI brain tumor segmentation using autoencoder regularizationCode0
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