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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 551–560 of 786 papers

TitleStatusHype
Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty—0
Tumor-Centered Patching for Enhanced Medical Image Segmentation—0
A Segmentation Foundation Model for Diverse-type Tumors—0
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training—0
Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning—0
Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location—0
Organ At Risk Segmentation with Multiple Modality—0
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
PAM-UNet: Shifting Attention on Region of Interest in Medical Images—0
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models—0
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