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 571–580 of 786 papers

TitleStatusHype
PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation—0
A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain—0
PINN-EMFNet: PINN-based and Enhanced Multi-Scale Feature Fusion Network for Breast Ultrasound Images Segmentation—0
Position Paper: Building Trust in Synthetic Data for Clinical AI—0
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks—0
Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning—0
Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images—0
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning—0
Prediction of Overall Survival of Brain Tumor Patients—0
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
Show:102550
← PrevPage 58 of 79Next →

No leaderboard results yet.