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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 211–220 of 786 papers

TitleStatusHype
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time AugmentationCode0
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)Code0
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisCode0
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing ModalitiesCode0
Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT SegmentationCode0
Co-Learning Feature Fusion Maps from PET-CT Images of Lung CancerCode0
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance ImagingCode0
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT VolumesCode0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
Head and Neck Tumor Segmentation of MRI from Pre- and Mid-radiotherapy with Pre-training, Data Augmentation and Dual Flow UNetCode0
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