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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 571–580 of 786 papers

TitleStatusHype
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities—0
Transfer learning for automatic brain tumor classification Using MRI Images.—0
Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival PredictionCode0
TransMed: Transformers Advance Multi-modal Medical Image Classification—0
Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation—0
PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images—0
Post-hoc Overall Survival Time Prediction from Brain MRICode0
Benefits of Linear Conditioning with Metadata for Image Segmentation—0
Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation—0
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint—0
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