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 181–190 of 786 papers

TitleStatusHype
A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor SegmentationCode0
Improving the U-Net Configuration for Automated Delineation of Head and Neck Cancer on MRICode0
SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label SmoothingCode0
A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor SegmentationCode0
Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss functionCode0
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)Code0
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmCode0
UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor SegmentationCode0
AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ SegmentationCode0
Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point PromptsCode0
Show:102550
← PrevPage 19 of 79Next →

No leaderboard results yet.