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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 481–490 of 786 papers

TitleStatusHype
Segmenting Brain Tumors with Symmetry—0
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging—0
Self-calibrated convolution towards glioma segmentation—0
Self-semantic contour adaptation for cross modality brain tumor segmentation—0
Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube—0
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout—0
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification—0
Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differences—0
Self-supervised Tumor Segmentation through Layer Decomposition—0
Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database—0
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