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 651–660 of 786 papers

TitleStatusHype
Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images—0
MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep LearningCode0
Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound ImagesCode0
Organ At Risk Segmentation with Multiple Modality—0
A New Three-stage Curriculum Learning Approach to Deep Network Based Liver Tumor SegmentationCode0
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation—0
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks—0
Semi-Supervised Variational Autoencoder for Survival PredictionCode0
Brain MRI Tumor Segmentation with Adversarial Networks—0
Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube—0
Show:102550
← PrevPage 66 of 79Next →

No leaderboard results yet.