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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 291–300 of 786 papers

TitleStatusHype
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival PredictionCode0
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing ModalitiesCode0
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative NormalizationCode0
Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessorCode0
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional NetworksCode0
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural NetworksCode0
Volumetric medical image segmentation through dual self-distillation in U-shaped networksCode0
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image SegmentationCode0
Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal DependencyCode0
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