SOTAVerified

Segmentation

Papers

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Showing 3276–3300 of 13072 papers

TitleStatusHype
Muscle volume quantification: guiding transformers with anatomical priors—0
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI—0
DistNet2D: Leveraging long-range temporal information for efficient segmentation and tracking—0
MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) DecoderCode1
Radiomics as a measure superior to the Dice similarity coefficient for tumor segmentation performance evaluation—0
Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation ModelsCode1
Modular Anti-noise Deep Learning Network for Robotic Grasp Detection Based on RGB ImagesCode0
Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic SegmentationCode1
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes—0
Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation—0
One-shot Localization and Segmentation of Medical Images with Foundation Models—0
Audio-Visual Instance SegmentationCode1
SmooSeg: Smoothness Prior for Unsupervised Semantic SegmentationCode1
CARIS: Context-Augmented Referring Image SegmentationCode1
Instance Segmentation under Occlusions via Location-aware Copy-Paste Data AugmentationCode1
Text Augmented Spatial-aware Zero-shot Referring Image Segmentation—0
Multivessel Coronary Artery Segmentation and Stenosis Localisation using Ensemble Learning—0
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation—0
AutoCT: Automated CT registration, segmentation, and quantification—0
Topic Segmentation of Semi-Structured and Unstructured Conversational Datasets using Language Models—0
Task-driven Prompt Evolution for Foundation Models—0
Technical Note: Feasibility of translating 3.0T-trained Deep-Learning Segmentation Models Out-of-the-Box on Low-Field MRI 0.55T Knee-MRI of Healthy Controls—0
Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic SegmentationCode0
Three Pillars improving Vision Foundation Model Distillation for LidarCode1
A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-raysCode1
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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