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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 491–500 of 786 papers

TitleStatusHype
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention—0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
Modality-Pairing Learning for Brain Tumor Segmentation—0
A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation—0
Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images—0
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network—0
Automated head and neck tumor segmentation from 3D PET/CT—0
MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning—0
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks—0
Brain MRI Tumor Segmentation with Adversarial Networks—0
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