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 421–430 of 786 papers

TitleStatusHype
Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation—0
Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation—0
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation—0
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data—0
Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction—0
Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans—0
Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering—0
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation—0
ONCOPILOT: A Promptable CT Foundation Model For Solid Tumor Evaluation—0
Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning—0
Show:102550
← PrevPage 43 of 79Next →

No leaderboard results yet.