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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 561–570 of 786 papers

TitleStatusHype
Annotation-efficient deep learning for automatic medical image segmentationCode1
Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)Code1
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation—0
Inter-slice Context Residual Learning for 3D Medical Image SegmentationCode1
Efficient embedding network for 3D brain tumor segmentation—0
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasetsCode1
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
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