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 541–550 of 786 papers

TitleStatusHype
A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRICode0
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout—0
Utilizing Attention, Linked Blocks, And Pyramid Pooling To Propel Brain Tumor Segmentation In 3DCode0
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images—0
Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT—0
Self-supervised Tumor Segmentation through Layer Decomposition—0
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation—0
Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation—0
Show:102550
← PrevPage 55 of 79Next →

No leaderboard results yet.