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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 431–440 of 786 papers

TitleStatusHype
Organ At Risk Segmentation with Multiple Modality—0
PAM-UNet: Shifting Attention on Region of Interest in Medical Images—0
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models—0
PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images—0
Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning—0
Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images—0
Partial Labeled Gastric Tumor Segmentation via patch-based Reiterative Learning—0
PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation—0
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training—0
PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation—0
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