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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 651–660 of 786 papers

TitleStatusHype
Self-supervised Tumor Segmentation through Layer Decomposition—0
Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database—0
Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning—0
Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation—0
2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation—0
An attempt at beating the 3D U-Net—0
Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy—0
Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model—0
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images—0
Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities—0
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