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 701–710 of 786 papers

TitleStatusHype
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networksCode0
M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient SupervisionCode0
Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound DiagnosisCode0
Survival prediction using ensemble tumor segmentation and transfer learningCode0
Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor SegmentationCode0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation ProblemsCode0
MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep LearningCode0
Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing TasksCode0
Exploring Feature Representation Learning for Semi-supervised Medical Image SegmentationCode0
3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRICode0
Show:102550
← PrevPage 71 of 79Next →

No leaderboard results yet.