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 671–680 of 786 papers

TitleStatusHype
Spatially Covariant Lesion Segmentation—0
Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition—0
Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI—0
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation—0
A Multi-View Dynamic Fusion Framework: How to Improve the Multimodal Brain Tumor Segmentation from Multi-Views?—0
Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation—0
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation—0
Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation—0
Variational U-Net with Local Alignment for Joint Tumor Extraction and Registration (VALOR-Net) of Breast MRI Data Acquired at Two Different Field Strengths—0
Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images—0
Show:102550
← PrevPage 68 of 79Next →

No leaderboard results yet.