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Segmentation

Papers

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Showing 6301–6325 of 13072 papers

TitleStatusHype
Topology-Aware Focal Loss for 3D Image Segmentation—0
Urban GeoBIM construction by integrating semantic LiDAR point clouds with as-designed BIM models—0
Semi-Supervised Semantic Segmentation With Region RelevanceCode0
Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net ArchitectureCode0
Med-Tuning: A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation—0
Deep Attention Unet: A Network Model with Global Feature Perception Ability—0
Ensembling Instance and Semantic Segmentation for Panoptic Segmentation—0
Brain tumor multi classification and segmentation in MRI images using deep learning—0
Boosting Semantic Segmentation with Semantic BoundariesCode0
Denoising Diffusion Medical Models—0
Few-shot Medical Image Segmentation via Cross-Reference Transformer—0
Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object SegmentationCode0
Realistic Data Enrichment for Robust Image Segmentation in Histopathology—0
Detection and Classification of Glioblastoma Brain Tumor—0
Structure Preserving Cycle-GAN for Unsupervised Medical Image Domain Adaptation—0
Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets—0
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging—0
Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networksCode0
DETR-based Layered Clothing Segmentation and Fine-Grained Attribute Recognition—0
One-shot and Partially-Supervised Cell Image Segmentation Using Small Visual PromptCode0
Learning to "Segment Anything" in Thermal Infrared Images through Knowledge Distillation with a Large Scale Dataset SATIRCode0
ProPanDL: A Modular Architecture for Uncertainty-Aware Panoptic Segmentation—0
When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation—0
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism—0
Collaborative Feature Learning for Fine-grained Facial Forgery Detection and 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