SOTAVerified

Segmentation

Papers

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Showing 6101–6125 of 13072 papers

TitleStatusHype
Automatic segmentation of lizard spots using an active contour model—0
Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging—0
Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images—0
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning—0
Active Image Segmentation Propagation—0
Deep Learning for Semantic Part Segmentation with High-Level Guidance—0
Fully Automated Deep Learning-enabled Detection for Hepatic Steatosis on Computed Tomography: A Multicenter International Validation Study—0
Deep Learning for Pancreas Segmentation: a Systematic Review—0
Automatic segmentation of kidney and liver tumors in CT images—0
Deep Learning for Multi-View Ultrasonic Image Fusion—0
Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities—0
Automatic Segmentation of Head and Neck Tumors and Nodal Metastases in PET-CT scans—0
Expert-Adaptive Medical Image Segmentation—0
Deep Learning for Medical Image Segmentation—0
Amodal Instance Segmentation with Diffusion Shape Prior Estimation—0
Active contours driven by local and global intensity fitting energy with application to SAR image segmentation and its fast solvers—0
3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction—0
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review—0
Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection—0
Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation—0
Automatic Segmentation of Dynamic Objects from an Image Pair—0
Deep learning for cardiac image segmentation: A review—0
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation—0
Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images—0
Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients—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