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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 461–470 of 786 papers

TitleStatusHype
Rel-UNet: Reliable Tumor Segmentation via Uncertainty Quantification in nnU-Net—0
Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing—0
RobU-Net: a heuristic robust multi-class brain tumor segmentation approaches for MRI scans—0
Robust Automatic Whole Brain Extraction on Magnetic Resonance Imaging of Brain Tumor Patients using Dense-Vnet—0
Robustifying deep networks for image segmentation—0
Robust Learning Protocol for Federated Tumor Segmentation Challenge—0
Robustness of Brain Tumor Segmentation—0
Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans—0
Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks—0
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation—0
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