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 641–650 of 786 papers

TitleStatusHype
Segmentation of brain tumor on magnetic resonance imaging using a convolutional architecture—0
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation—0
A Bayesian approach to tissue-fraction estimation for oncological PET segmentation—0
Region of Interest Identification for Brain Tumors in Magnetic Resonance Images—0
Technical report: Kidney tumor segmentation using a 2D U-Net followed by a statistical post-processing filter—0
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation—0
Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple Image Scales—0
Distributionally Robust Deep Learning using Hardness Weighted SamplingCode0
Transfer Learning for Brain Tumor Segmentation—0
Robustness of Brain Tumor Segmentation—0
Show:102550
← PrevPage 65 of 79Next →

No leaderboard results yet.