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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
United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI—0
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation—0
Unpaired cross-modality educed distillation (CMEDL) for medical image segmentation—0
Unsupervised Brain Tumor Segmentation with Image-based Prompts—0
Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning—0
Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy—0
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images—0
Variational U-Net with Local Alignment for Joint Tumor Extraction and Registration (VALOR-Net) of Breast MRI Data Acquired at Two Different Field Strengths—0
VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation—0
Volumetric Attention for 3D Medical Image Segmentation and Detection—0
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