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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 581–590 of 786 papers

TitleStatusHype
PriorNet: lesion segmentation in PET-CT including prior tumor appearance information—0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—0
Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement—0
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism—0
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans—0
A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation—0
A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation—0
A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images—0
PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation—0
Two Stage Segmentation of Cervical Tumors using PocketNet—0
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