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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 721–730 of 786 papers

TitleStatusHype
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization—0
Decentralized Differentially Private Segmentation with PATE—0
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation—0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI—0
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images—0
Synthetic Data as Validation—0
Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation—0
Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation—0
Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation—0
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings—0
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