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 411–420 of 786 papers

TitleStatusHype
Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence—0
The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)—0
Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI—0
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans—0
BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images—0
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation—0
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma—0
Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net—0
Show:102550
← PrevPage 42 of 79Next →

No leaderboard results yet.