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 461–470 of 786 papers

TitleStatusHype
Weakly supervised pan-cancer segmentation tool—0
Automatic Liver Lesion Detection using Cascaded Deep Residual Networks—0
MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts—0
MBA-Net: SAM-driven Bidirectional Aggregation Network for Ovarian Tumor Segmentation—0
Deep segmentation networks predict survival of non-small cell lung cancer—0
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation—0
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations—0
Medical Image Analysis using Deep Relational Learning—0
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation—0
Show:102550
← PrevPage 47 of 79Next →

No leaderboard results yet.