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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 341–350 of 786 papers

TitleStatusHype
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)—0
Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images—0
Covariance Self-Attention Dual Path UNet for Rectal Tumor Segmentation—0
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy—0
Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge—0
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images—0
Context Aware 3D UNet for Brain Tumor Segmentation—0
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network—0
Confidence Intervals for Performance Estimates in Brain MRI Segmentation—0
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