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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 611–620 of 786 papers

TitleStatusHype
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation—0
Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing—0
Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks—0
An Exceptional Dataset For Rare Pancreatic Tumor Segmentation—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
A Bayesian approach to tissue-fraction estimation for oncological PET segmentation—0
Robustness of Brain Tumor Segmentation—0
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