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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 441–450 of 786 papers

TitleStatusHype
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks—0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging—0
TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical ImagesCode2
Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers—0
SoftDropConnect (SDC) -- Effective and Efficient Quantification of the Network Uncertainty in Deep MR Image AnalysisCode0
Automatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are?Code0
Self-semantic contour adaptation for cross modality brain tumor segmentation—0
Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning—0
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