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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 571–580 of 786 papers

TitleStatusHype
DR-Unet104 for Multimodal MRI brain tumor segmentationCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challengeCode1
nnU-Net for Brain Tumor SegmentationCode1
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solutionCode1
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint—0
Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction—0
Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?—0
What is the best data augmentation for 3D brain tumor segmentation?Code1
Context Aware 3D UNet for Brain Tumor Segmentation—0
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