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 341–350 of 786 papers

TitleStatusHype
Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain TumorsCode0
Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound DiagnosisCode0
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet—0
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation—0
Complementary Information Mutual Learning for Multimodality Medical Image Segmentation—0
Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy—0
Integrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images—0
Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty PredictionCode0
Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction—0
Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations—0
Show:102550
← PrevPage 35 of 79Next →

No leaderboard results yet.