SOTAVerified

Segmentation

Papers

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Showing 1601–1625 of 13072 papers

TitleStatusHype
Performance Evaluation of YOLOv8 Model Configurations, for Instance Segmentation of Strawberry Fruit Development Stages in an Open Field Environment—0
Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRICode1
PRISM Lite: A lightweight model for interactive 3D placenta segmentation in ultrasoundCode1
Assessment of Cell Nuclei AI Foundation Models in Kidney PathologyCode0
One Shot is Enough for Sequential Infrared Small Target SegmentationCode0
Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis—0
ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary SegmentationCode2
In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic SegmentationCode2
Geo-UNet: A Geometrically Constrained Neural Framework for Clinical-Grade Lumen Segmentation in Intravascular Ultrasound—0
Embodied Uncertainty-Aware Object Segmentation—0
Novel adaptation of video segmentation to 3D MRI: efficient zero-shot knee segmentation with SAM2—0
SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and MoreCode5
Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs—0
Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation—0
SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation—0
Is SAM 2 Better than SAM in Medical Image Segmentation?—0
Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation—0
Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image SegmentationCode0
Path-SAM2: Transfer SAM2 for digital pathology semantic segmentationCode1
Counterfactuals and Uncertainty-Based Explainable Paradigm for the Automated Detection and Segmentation of Renal Cysts in Computed Tomography Images: A Multi-Center Study—0
Hybrid diffusion models: combining supervised and generative pretraining for label-efficient fine-tuning of segmentation models—0
Post-Mortem Human Iris Segmentation Analysis with Deep Learning—0
Biomedical Image Segmentation: A Systematic Literature Review of Deep Learning Based Object Detection Methods—0
Segment Anything in Medical Images and Videos: Benchmark and DeploymentCode7
Biomedical SAM 2: Segment Anything in Biomedical Images and VideosCode0
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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