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
3D Self-Supervised Methods for Medical ImagingCode1
A Joint Graph and Image Convolution Network for Automatic Brain Tumor SegmentationCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and AnalysisCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
Show:102550
← PrevPage 4 of 79Next →

No leaderboard results yet.