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Active Learning for Delineation of Curvilinear Structures

https://doi.org/10.1109/cvpr.2016.565

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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A Sequential Algorithm for Training Text Classifiers

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Microsoft COCO: Common Objects in Context

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Mind the Gap: Modeling Local and Global Context in (Road) Networks

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A Machine Learning Based Approach to Fiber Tractography Using Classifier Voting

inferredcites →https://doi.org/10.1007/978-3-642-22092-0_3openalex-snapshot

Combining Generative and Discriminative Models for Semantic Segmentation of CT Scans via Active Learning

inferredcites →https://doi.org/10.1007/978-3-642-38868-2_28openalex-snapshot

Hierarchical Discriminative Framework for Detecting Tubular Structures in 3D Images

inferredcites →https://doi.org/10.1007/978-3-642-40811-3_66openalex-snapshot

Supervised Feature Learning for Curvilinear Structure Segmentation

inferredcites →https://doi.org/10.1007/s12021-014-9253-2openalex-snapshot

Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models

inferredcites →https://doi.org/10.1016/b978-1-55860-377-6.50027-xopenalex-snapshot

Committee-Based Sampling For Training Probabilistic Classifiers

inferredcites →https://doi.org/10.1016/j.isprsjprs.2015.07.002openalex-snapshot

Road networks as collections of minimum cost paths

inferredcites →https://doi.org/10.1021/ci049810aopenalex-snapshot

Active Learning with Support Vector Machine Applied to Gene Expression Data for Cancer Classification

inferredcites →https://doi.org/10.1023/a:1017924227920openalex-snapshot

Reinforcement Learning for Call Admission Control and Routing under Quality of Service Constraints in Multimedia Networks

inferredcites →https://doi.org/10.1109/cvpr.2010.5540044openalex-snapshot

Beyond active noun tagging: Modeling contextual interactions for multi-class active learning

inferredcites →https://doi.org/10.1109/cvpr.2012.6247722openalex-snapshot

Automated reconstruction of tree structures using path classifiers and Mixed Integer Programming

inferredcites →https://doi.org/10.1109/cvpr.2012.6248108openalex-snapshot

RALF: A reinforced active learning formulation for object class recognition

inferredcites →https://doi.org/10.1109/cvpr.2013.116openalex-snapshot

Adaptive Active Learning for Image Classification

inferredcites →https://doi.org/10.1109/cvpr.2013.238openalex-snapshot

Reconstructing Loopy Curvilinear Structures Using Integer Programming

inferredcites →https://doi.org/10.1109/cvpr.2014.79openalex-snapshot

Hierarchical Subquery Evaluation for Active Learning on a Graph

inferredcites →https://doi.org/10.1109/iccv.2007.4408844openalex-snapshot

Active Learning with Gaussian Processes for Object Categorization

inferredcites →https://doi.org/10.1109/iccv.2013.373openalex-snapshot

Active Visual Recognition with Expertise Estimation in Crowdsourcing

inferredcites →https://doi.org/10.1109/iccv.2015.340openalex-snapshot

Introducing Geometry in Active Learning for Image Segmentation

inferredcites →https://doi.org/10.1137/1.9781611972771.27openalex-snapshot

Efficient Multiclass Boosting Classification with Active Learning

inferredcites →https://doi.org/10.1613/jair.295openalex-snapshot

Active Learning with Statistical Models

inferredcites →https://doi.org/10.3115/1613715.1613855openalex-snapshot

An analysis of active learning strategies for sequence labeling tasks

inferredcites →https://doi.org/10.48550/arxiv.1207.1403openalex-snapshot

Obtaining Calibrated Probabilities from Boosting

inferredcites →https://doi.org/10.5244/c.25.78openalex-snapshot

Combining Self Training and Active Learning for Video Segmentation

inferredcites →https://doi.org/10.5555/188490.188495openalex-snapshot

A sequential algorithm for training text classifiers

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/mp.13264openalex-snapshot

Deep learning in medical imaging and radiation therapy

inferred← citeshttps://doi.org/10.1007/978-3-030-04182-3_41openalex-snapshot

Active Learning Methods with Deep Gaussian Processes

inferred← citeshttps://doi.org/10.1007/978-3-319-67534-3_12openalex-snapshot

Expected Exponential Loss for Gaze-Based Video and Volume Ground Truth Annotation

inferred← citeshttps://doi.org/10.1016/j.cviu.2019.01.007openalex-snapshot

Geometry in active learning for binary and multi-class image segmentation

inferred← citeshttps://doi.org/10.1016/j.knosys.2019.105044openalex-snapshot

Promoting active learning with mixtures of Gaussian processes

inferred← citeshttps://doi.org/10.1016/j.knosys.2021.107639openalex-snapshot

Robust active representation via ℓ2,p -norm constraints

inferred← citeshttps://doi.org/10.1109/cvpr.2019.00870openalex-snapshot

Topology Reconstruction of Tree-Like Structure in Images via Structural Similarity Measure and Dominant Set Clustering

inferred← citeshttps://doi.org/10.1109/iccvw54120.2021.00194openalex-snapshot

All you need are a few pixels: semantic segmentation with PixelPick

inferred← citeshttps://doi.org/10.1109/tcsvt.2021.3079991openalex-snapshot

Active Learning Based 3D Semantic Labeling From Images and Videos

inferred← citeshttps://doi.org/10.48550/arxiv.1810.09726openalex-snapshot

CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.