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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 771–780 of 786 papers

TitleStatusHype
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation—0
E^2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans—0
Volumetric Attention for 3D Medical Image Segmentation and Detection—0
Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)—0
Efficient embedding network for 3D brain tumor segmentation—0
Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis—0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
Brain Tumor Segmentation on MRI with Missing Modalities—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation—0
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