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Segmentation

Papers

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Showing 4951–4975 of 13072 papers

TitleStatusHype
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge—0
Semantic Layering in Room Segmentation via LLMs—0
Class and Region-Adaptive Constraints for Network CalibrationCode0
Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets—0
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency—0
Embarrassingly Simple Scribble Supervision for 3D Medical Segmentation—0
Object Segmentation-Assisted Inter Prediction for Versatile Video Coding—0
Augment Before Copy-Paste: Data and Memory Efficiency-Oriented Instance Segmentation Framework for Sport-scenes—0
A Spatial-Temporal Progressive Fusion Network for Breast Lesion Segmentation in Ultrasound Videos—0
MISS: Memory-efficient Instance Segmentation Framework By Visual Inductive Priors Flow Propagation—0
Circle Representation for Medical Instance Object SegmentationCode0
TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models—0
Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy SegmentationCode0
EffiPerception: an Efficient Framework for Various Perception Tasks—0
MaskDiffusion: Exploiting Pre-trained Diffusion Models for Semantic SegmentationCode0
A Dynamically Weighted Loss Function for Unsupervised Image Segmentation—0
Ensembling and Test Augmentation for Covid-19 Detection and Covid-19 Domain Adaptation from 3D CT-Scans—0
MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy Statistical Shape Modeling Directly From Images—0
Fuzzy Rank-based Late Fusion Technique for Cytology image Segmentation—0
Topologically Faithful Multi-class Segmentation in Medical Images—0
COVID-CT-H-UNet: a novel COVID-19 CT segmentation network based on attention mechanism and Bi-category Hybrid loss—0
Uncertainty-Aware Adapter: Adapting Segment Anything Model (SAM) for Ambiguous Medical Image Segmentation—0
Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentation—0
TransLandSeg: A Transfer Learning Approach for Landslide Semantic Segmentation Based on Vision Foundation Model—0
Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor SegmentationCode0
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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