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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 211–220 of 786 papers

TitleStatusHype
Class Balanced PixelNet for Neurological Image Segmentation—0
Clinical Inspired MRI Lesion Segmentation—0
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images—0
Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation—0
Deepfake Image Generation for Improved Brain Tumor Segmentation—0
CAFCT-Net: A CNN-Transformer Hybrid Network with Contextual and Attentional Feature Fusion for Liver Tumor Segmentation—0
Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining—0
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches—0
Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection—0
ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation—0
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