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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 241–250 of 786 papers

TitleStatusHype
Glioblastoma Tumor Segmentation using an Ensemble of Vision TransformersCode0
Hybrid-Fusion Transformer for Multisequence MRICode0
Radiomics as a measure superior to the Dice similarity coefficient for tumor segmentation performance evaluation—0
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation—0
Synthetic Data as Validation—0
Progressive Dual Priori Network for Generalized Breast Tumor SegmentationCode0
Whole-brain radiomics for clustered federated personalization in brain tumor segmentationCode0
MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning—0
RT-SRTS: Angle-Agnostic Real-Time Simultaneous 3D Reconstruction and Tumor Segmentation from Single X-Ray ProjectionCode0
3D TransUNet: Advancing Medical Image Segmentation through Vision TransformersCode4
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