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
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point PromptsCode0
Swin UNETR++: Advancing Transformer-Based Dense Dose Prediction Towards Fully Automated Radiation Oncology Treatments—0
Glioblastoma Tumor Segmentation using an Ensemble of Vision TransformersCode0
Hybrid-Fusion Transformer for Multisequence MRICode0
Radiomics as a measure superior to the Dice similarity coefficient for tumor segmentation performance evaluation—0
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation—0
Synthetic Data as Validation—0
Progressive Dual Priori Network for Generalized Breast Tumor SegmentationCode0
MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning—0
Show:102550
← PrevPage 37 of 79Next →

No leaderboard results yet.