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 31–40 of 786 papers

TitleStatusHype
Brain Tumor Segmentation with Deep Neural NetworksCode1
3D Self-Supervised Methods for Medical ImagingCode1
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
Show:102550
← PrevPage 4 of 79Next →

No leaderboard results yet.