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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 181–190 of 786 papers

TitleStatusHype
A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation—0
Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation—0
A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation—0
Cross-Organ Domain Adaptive Neural Network for Pancreatic Endoscopic Ultrasound Image Segmentation—0
CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset—0
AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients—0
Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images—0
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
A Hybrid Framework for Tumor Saliency Estimation—0
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