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 451–460 of 786 papers

TitleStatusHype
QuantU-Net: Efficient Wearable Medical Imaging Using Bitwidth as a Trainable Parameter—0
QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors—0
Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation—0
Radiomics as a measure superior to the Dice similarity coefficient for tumor segmentation performance evaluation—0
RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging—0
Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation—0
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs—0
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
Region of Interest Identification for Brain Tumors in Magnetic Resonance Images—0
Relevance analysis of MRI sequences for automatic liver tumor segmentation—0
Show:102550
← PrevPage 46 of 79Next →

No leaderboard results yet.