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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 391–400 of 786 papers

TitleStatusHype
Automated ensemble method for pediatric brain tumor segmentation—0
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data—0
SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation—0
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation—0
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation—0
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative NormalizationCode0
Deepfake Image Generation for Improved Brain Tumor Segmentation—0
Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model—0
Confidence Intervals for Performance Estimates in Brain MRI Segmentation—0
A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation—0
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