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 41–50 of 786 papers

TitleStatusHype
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
3D Self-Supervised Methods for Medical ImagingCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion ModelsCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
Annotation-efficient deep learning for automatic medical image segmentationCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
Show:102550
← PrevPage 5 of 79Next →

No leaderboard results yet.