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 261–270 of 786 papers

TitleStatusHype
A Review on End-To-End Methods for Brain Tumor Segmentation and Overall Survival Prediction—0
Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients—0
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans—0
Brain Tumor Survival Prediction using Radiomics Features—0
E^2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans—0
Brain tumor segmentation with missing modalities via latent multi-source correlation representation—0
A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain—0
AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation—0
Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)—0
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images—0
Show:102550
← PrevPage 27 of 79Next →

No leaderboard results yet.