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 141–150 of 786 papers

TitleStatusHype
3D Self-Supervised Methods for Medical ImagingCode1
Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance ImagingCode1
Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image SynthesisCode1
DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR ImagesCode1
Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor SegmentationCode1
Weakly supervised multiple instance learning histopathological tumor segmentationCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
Vox2Vox: 3D-GAN for Brain Tumour SegmentationCode1
Synthesize then Compare: Detecting Failures and Anomalies for Semantic SegmentationCode1
Show:102550
← PrevPage 15 of 79Next →

No leaderboard results yet.