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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 311–320 of 786 papers

TitleStatusHype
3D MRI brain tumor segmentation using autoencoder regularizationCode0
Exploring Feature Representation Learning for Semi-supervised Medical Image SegmentationCode0
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time AugmentationCode0
multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical InformationCode0
Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion modelsCode0
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance ImagingCode0
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images—0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI—0
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation—0
Decentralized Differentially Private Segmentation with PATE—0
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