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 461–470 of 786 papers

TitleStatusHype
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRICode1
Extending nn-UNet for brain tumor segmentationCode1
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation—0
FIBA: Frequency-Injection based Backdoor Attack in Medical Image AnalysisCode1
Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data—0
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning—0
Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessorCode0
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
Show:102550
← PrevPage 47 of 79Next →

No leaderboard results yet.