SOTAVerified

Segmentation

Papers

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Showing 9051–9075 of 13072 papers

TitleStatusHype
A Bag of Words Approach for Semantic Segmentation of Monitored Scenes—0
Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge—0
AutoFish: Dataset and Benchmark for Fine-grained Analysis of Fish—0
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation—0
AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes—0
AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation—0
Autoencoders for Multi-Label Prostate MR Segmentation—0
Extending nnU-Net is all you need—0
Multi-target and multi-stage liver lesion segmentation and detection in multi-phase computed tomography scans—0
Patch Correspondences for Interpreting Pixel-level CNNs—0
External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data—0
Extracting Lungs from CT Images using Fully Convolutional Networks—0
Extracting Semantic Knowledge from GANs with Unsupervised Learning—0
Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses—0
Extraction of Line Word Character Segments Directly from Run Length Compressed Printed Text Documents—0
Extraction of Skin Lesions from Non-Dermoscopic Images Using Deep Learning—0
Extraction of the Argument Structure of Tokyo Metropolitan Assembly Minutes: Segmentation of Question-and-Answer Sets—0
Extraction of Vascular Wall in Carotid Ultrasound via a Novel Boundary-Delineation Network—0
AutoCT: Automated CT registration, segmentation, and quantification—0
Extreme Augmentation : Can deep learning based medical image segmentation be trained using a single manually delineated scan?—0
Multi-task 3D building understanding with multi-modal pretraining—0
ExtremeMETA: High-speed Lightweight Image Segmentation Model by Remodeling Multi-channel Metamaterial Imagers—0
Extreme Point Supervised Instance Segmentation—0
Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles—0
EyeLoveGAN: Exploiting domain-shifts to boost network learning with cycleGANs—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