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 671–680 of 786 papers

TitleStatusHype
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation—0
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty AnalysisCode1
Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge—0
Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation—0
End-to-End Boundary Aware Networks for Medical Image Segmentation—0
Multi-step Cascaded Networks for Brain Tumor SegmentationCode0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—0
Mask Mining for Improved Liver Lesion Segmentation—0
Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation—0
Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit—0
Show:102550
← PrevPage 68 of 79Next →

No leaderboard results yet.