SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 361–370 of 786 papers

TitleStatusHype
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation—0
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation—0
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors—0
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet—0
Fully Automatic Brain Tumor Segmentation using a Normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow—0
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models—0
Efficient embedding network for 3D brain tumor segmentation—0
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
Show:102550
← PrevPage 37 of 79Next →

No leaderboard results yet.