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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
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation—0
Efficient embedding network for 3D brain tumor segmentation—0
SoftSeg: Advantages of soft versus binary training for image segmentation—0
Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation—0
Automatic Brain Tumor Segmentation with Scale Attention NetworkCode0
Covariance Self-Attention Dual Path UNet for Rectal Tumor Segmentation—0
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint—0
Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction—0
Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?—0
Context Aware 3D UNet for Brain Tumor Segmentation—0
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