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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 461–470 of 786 papers

TitleStatusHype
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images—0
ISA-Net: Improved spatial attention network for PET-CT tumor segmentation—0
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images—0
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation—0
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation—0
Hyper-Connected Transformer Network for Multi-Modality PET-CT Segmentation—0
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisCode0
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time AugmentationCode0
Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks—0
Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location—0
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