SOTAVerified

Segmentation

Papers

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Showing 6051–6075 of 13072 papers

TitleStatusHype
Influence of segmentation accuracy in structural MR head scans on electric field computation for TMS and tES—0
Influence of segmentation on deep iris recognition performance—0
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet—0
Segmentation of the Left Ventricle by SDD double threshold selection and CHT—0
Fully automated quantification of in vivo viscoelasticity of prostate zones using magnetic resonance elastography with Dense U-net segmentation—0
Information Entropy Based Feature Pooling for Convolutional Neural Networks—0
ArticulatedFusion: Real-time Reconstruction of Motion, Geometry and Segmentation Using a Single Depth Camera—0
Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks—0
Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling—0
Fully Automated Organ Segmentation in Male Pelvic CT Images—0
Inner Cell Mass and Trophectoderm Segmentation in Human Blastocyst Images using Deep Neural Network—0
Inpainting-Driven Mask Optimization for Object Removal—0
Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Segmentation across Disjoint Labels—0
In pixels we trust: From Pixel Labeling to Object Localization and Scene Categorization—0
Fully Automated Non-rigid Segmentation with Distance Regularized Level Set Evolution Initialized and Constrained by Deep-structured Inference—0
Learning Confident Classifiers in the Presence of Label Noise—0
INS-Conv: Incremental Sparse Convolution for Online 3D Segmentation—0
Insight Any Instance: Promptable Instance Segmentation for Remote Sensing Images—0
Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks—0
Inspecting Model Fairness in Ultrasound Segmentation Tasks—0
ClusterNet: 3D Instance Segmentation in RGB-D Images—0
ARPM-net: A novel CNN-based adversarial method with Markov Random Field enhancement for prostate and organs at risk segmentation in pelvic CT images—0
Instance and Panoptic Segmentation Using Conditional Convolutions—0
Instance-aware 3D Semantic Segmentation powered by Shape Generators and Classifiers—0
Fully Automated Machine Learning Pipeline for Echocardiogram Segmentation—0
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All datasetsSA-1B.MFSDMMFloodSimGas

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1unSAM+ (Semi-supervised)Average Precision42.8—Unverified
2SAMAverage Precision38.9—Unverified
#ModelMetricClaimedVerifiedStatus
1HNN10%20—Unverified
#ModelMetricClaimedVerifiedStatus
1ABANetF1 Score96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50 + DeepLabV3+F1 score0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1LangGasIoU0.69—Unverified