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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 371–380 of 786 papers

TitleStatusHype
Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)—0
Benefits of Linear Conditioning with Metadata for Image Segmentation—0
A Bayesian approach to tissue-fraction estimation for oncological PET segmentation—0
Glioblastoma Multiforme Patient Survival Prediction—0
Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks—0
Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via Multi-task Semi-supervised Learning—0
Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation—0
Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation—0
Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients—0
Belief function-based semi-supervised learning for brain tumor segmentation—0
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