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 701–710 of 786 papers

TitleStatusHype
Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images—0
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation—0
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy—0
Covariance Self-Attention Dual Path UNet for Rectal Tumor Segmentation—0
Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images—0
Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars—0
A Multiscale Patch Based Convolutional Network for Brain Tumor Segmentation—0
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets—0
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation—0
Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset—0
Show:102550
← PrevPage 71 of 79Next →

No leaderboard results yet.