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Segmentation

Papers

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Showing 6001–6025 of 13072 papers

TitleStatusHype
Automatic text extraction and character segmentation using maximally stable extremal regions—0
Deep Recurrent Level Set for Segmenting Brain Tumors—0
DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method—0
Automatic Syllabification for Manipuri language—0
Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation—0
3D Skin Segmentation Methods in Medical Imaging: A Comparison—0
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings—0
Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net—0
Automatic Stroke Lesions Segmentation in Diffusion-Weighted MRI—0
DeepPyramid+: Medical Image Segmentation using Pyramid View Fusion and Deformable Pyramid Reception—0
Automatic Spine Segmentation using Convolutional Neural Network via Redundant Generation of Class Labels for 3D Spine Modeling—0
DeepPyram: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos—0
Deep Probability Segmentation: Are segmentation models probability estimators?—0
Deep Polarization Cues for Transparent Object Segmentation—0
Automatic Skin Lesion Segmentation using Semi-supervised Learning Technique—0
A multilevel thresholding algorithm using Electromagnetism Optimization—0
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation—0
Deep Patch-based Human Segmentation—0
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation—0
Deep Neural Patchworks: Coping with Large Segmentation Tasks—0
Deep Neural Networks With Region-Based Pooling Structures for Mammographic Image Classification—0
Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation—0
A Multilayer-Based Framework for Online Background Subtraction with Freely Moving Cameras—0
Active learning using adaptable task-based prioritisation—0
Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic 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