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Segmentation

Papers

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Showing 5851–5875 of 13072 papers

TitleStatusHype
Impact of Label Types on Training SWIN Models with Overhead Imagery—0
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation—0
Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction—0
Co-occurrence Background Model with Superpixels for Robust Background Initialization—0
Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT images—0
Keypoint Transfer for Fast Whole-Body Segmentation—0
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities—0
Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices—0
Learning Stixel-based Instance Segmentation—0
FusionSeg: Learning to Combine Motion and Appearance for Fully Automatic Segmentation of Generic Objects in Videos—0
Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images—0
FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos—0
FusionSAM: Latent Space driven Segment Anything Model for Multimodal Fusion and Segmentation—0
Implicit segmentation of Kannada characters in offline handwriting recognition using hidden Markov models—0
Implicit Tubular Surface Generation Guided by Centerline—0
Implicit U-Net for volumetric medical image segmentation—0
Coarse Retinal Lesion Annotations Refinement via Prototypical Learning—0
Fusion of Image Segmentation Algorithms using Consensus Clustering—0
Attention on Classification for Fire Segmentation—0
Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets—0
3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management—0
Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study—0
Importance of Self-Consistency in Active Learning for Semantic Segmentation—0
Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans—0
Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network—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