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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 601–610 of 786 papers

TitleStatusHype
E^2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans—0
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-Domain Image Completion for Random Missing Input Data—0
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNetCode1
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional NetworksCode0
DSU-net: Dense SegU-net for automatic head-and-neck tumor segmentation in MR images—0
3D Self-Supervised Methods for Medical ImagingCode1
Robust Automatic Whole Brain Extraction on Magnetic Resonance Imaging of Brain Tumor Patients using Dense-Vnet—0
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