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 201–210 of 786 papers

TitleStatusHype
Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality imagesCode0
EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy—0
Tumor segmentation on whole slide images: training or prompting?—0
Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion modelsCode0
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor SegmentationCode0
Self-calibrated convolution towards glioma segmentation—0
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network—0
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser—0
Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model—0
CAFCT-Net: A CNN-Transformer Hybrid Network with Contextual and Attentional Feature Fusion for Liver Tumor Segmentation—0
Show:102550
← PrevPage 21 of 79Next →

No leaderboard results yet.