Semi-automatic data annotation based on feature-space projection and local quality metrics: An application to cerebral emboli characterization

Semi-automatic data annotation based on feature-space projection and local quality metrics: An application to cerebral emboli characterization

We propose a semi-supervised learning approach to annotate a dataset with reduced requirements for manual annotation and with controlled annotation error. The method is based on feature-space projection and label propagation using local quality metrics. First, an auto-encoder extracts the features o...

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Bibliographic Details
Main Authors: Almar, M. (Author), Delachartre, P. (Author), Guépié, B.K (Author), Roux, E. (Author), Vindas, Y. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2022
Subjects:
Annotation errors
Classification (of information)
Data annotation
Economic and social effects
Emboli characterization
Embolus characterization
Errors
Feature space
Label propagation
Learning algorithms
Motion compensation
Nearest neighbor search
Nearest-neighbour
Noisy labels
Quality metrices
Semi-automatics
Semi-supervised learning
Stochastic systems
Stroke
Supervised learning
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