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Segmentation

Papers

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Showing 6476–6500 of 13072 papers

TitleStatusHype
ReFit: A Framework for Refinement of Weakly Supervised Semantic Segmentation using Object Border Fitting for Medical ImagesCode0
An automated pipeline to create an atlas of in situ hybridization gene expression data in the adult marmoset brainCode0
SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs—0
Object-Centric Multi-Task Learning for Human Instances—0
Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images—0
One-Shot Segmentation of Novel White Matter Tracts via Extensive Data AugmentationCode0
Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution SegmentationCode0
OSIS: Efficient One-stage Network for 3D Instance Segmentation—0
Spatial Correspondence between Graph Neural Network-Segmented Images—0
Towards Universal Vision-language Omni-supervised Segmentation—0
Ensemble Learning of Myocardial Displacements for Myocardial Infarction Detection in EchocardiographyCode0
Token Sparsification for Faster Medical Image SegmentationCode0
Dynamic Y-KD: A Hybrid Approach to Continual Instance Segmentation—0
Score-Based Generative Models for Medical Image Segmentation using Signed Distance Functions—0
Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN ImagesCode0
Explainable Semantic Medical Image Segmentation with Style—0
Estimating friction coefficient using generative modelling—0
Segmentation method for cerebral blood vessels from MRA using hysteresisCode0
TAEC: Unsupervised Action Segmentation with Temporal-Aware Embedding and Clustering—0
Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI ScansCode0
Retinal Image Segmentation with Small Datasets—0
Open-world Instance Segmentation: Top-down Learning with Bottom-up Supervision—0
Rethinking Range View Representation for LiDAR Segmentation—0
CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image SegmentationCode0
SSL^2: Self-Supervised Learning meets Semi-Supervised Learning: Multiple Sclerosis Segmentation in 7T-MRI from large-scale 3T-MRI—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