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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 311–320 of 786 papers

TitleStatusHype
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation—0
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma—0
Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net—0
Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty—0
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification—0
Flexible Fusion Network for Multi-modal Brain Tumor Segmentation—0
Brain Tumor Segmentation from MRI Images using Deep Learning Techniques—0
Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reportingCode1
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