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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 421–430 of 786 papers

TitleStatusHype
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
3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation—0
Topology-Aware Focal Loss for 3D Image Segmentation—0
FedPIDAvg: A PID controller inspired aggregation method for Federated Learning—0
When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation—0
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism—0
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning—0
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