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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

Showing 131140 of 786 papers

TitleStatusHype
nnU-Net for Brain Tumor SegmentationCode1
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solutionCode1
What is the best data augmentation for 3D brain tumor segmentation?Code1
ivadomed: A Medical Imaging Deep Learning ToolboxCode1
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentationCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNetCode1
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