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Segmentation

Papers

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Showing 7976–8000 of 13072 papers

TitleStatusHype
Correlation-aware active learning for surgery video segmentation—0
Improved Image Boundaries for Better Video Segmentation—0
Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography—0
Correlation Adaptive Subspace Segmentation by Trace Lasso—0
Attention Toward Neighbors: A Context Aware Framework for High Resolution Image Segmentation—0
Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks—0
Improved-Flow Warp Module for Remote Sensing Semantic Segmentation—0
Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net—0
CORPS: Cost-free Rigorous Pseudo-labeling based on Similarity-ranking for Brain MRI Segmentation—0
Attention to Scale: Scale-aware Semantic Image Segmentation—0
Multi-Task Learning via Scale Aware Feature Pyramid Networks and Effective Joint Head—0
A Hierarchical Multi-Task Approach to Gastrointestinal Image Analysis—0
Improved AI-based segmentation of apical and basal slices from clinical cine CMR—0
Coronary Plaque Analysis for CT Angiography Clinical Research—0
Multitask Network for Joint Object Detection, Semantic Segmentation and Human Pose Estimation in Vehicle Occupancy Monitoring—0
Coronary artery segmentation in non-contrast calcium scoring CT images using deep learning—0
Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans—0
Importance of Self-Consistency in Active Learning for Semantic Segmentation—0
Multi-Task Self-Supervised Learning for Image Segmentation Task—0
Coronary Artery Segmentation in Angiographic Videos Using A 3D-2D CE-Net—0
MultiTaskVIF: Segmentation-oriented visible and infrared image fusion via multi-task learning—0
Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study—0
Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans—0
Coronary Artery Segmentation from Intravascular Optical Coherence Tomography Using Deep Capsules—0
AttentionShift: Iteratively Estimated Part-Based Attention Map for Pointly Supervised Instance 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