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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 231–240 of 786 papers

TitleStatusHype
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy—0
Covariance Self-Attention Dual Path UNet for Rectal Tumor Segmentation—0
Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images—0
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives—0
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)—0
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets—0
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation—0
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
Deep Recurrent Level Set for Segmenting Brain Tumors—0
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