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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 321–330 of 786 papers

TitleStatusHype
Mask Mining for Improved Liver Lesion Segmentation—0
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation—0
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing—0
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation—0
Evaluating the Impact of Sequence Combinations on Breast Tumor Segmentation in Multiparametric MRI—0
Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation—0
Brain Tumor Detection Based On Mathematical Analysis and Symmetry Information—0
EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy—0
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation—0
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