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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
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Towards annotation-efficient segmentation via image-to-image translation—0
Federated brain tumor segmentation: an extensive benchmark—0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
Mask Mining for Improved Liver Lesion Segmentation—0
Bottleneck Supervised U-Net for Pixel-wise Liver and Tumor Segmentation—0
Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation—0
Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation—0
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
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