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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
Multi-step Cascaded Networks for Brain Tumor SegmentationCode0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—0
Mask Mining for Improved Liver Lesion Segmentation—0
Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit—0
Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation—0
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
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