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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 751–760 of 786 papers

TitleStatusHype
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation—0
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023—0
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation—0
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes—0
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser—0
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning—0
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans—0
Brain Tumor Survival Prediction using Radiomics Features—0
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