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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 681–690 of 786 papers

TitleStatusHype
Kidney and Kidney Tumor Segmentation using a Logical Ensemble of U-nets with Volumetric Validation—0
An attempt at beating the 3D U-Net—0
Automatic segmentation of kidney and liver tumors in CT images—0
A Structural Graph-Based Method for MRI Analysis—0
Robustifying deep networks for image segmentation—0
Hierarchical Fine-Tuning for joint Liver Lesion Segmentation and Lesion Classification in CT—0
Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation—0
Relevance analysis of MRI sequences for automatic liver tumor segmentation—0
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors—0
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation—0
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