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A Framework for Forming Middle Distance Routes Based on Spatial Guidelines, Perceived Accessibility and Visual Cues in Smart City Read chapter Read first chapter

2022 | OriginalPaper | Chapter

An Advanced Tool for Semi-automatic Annotation for Early Screening of Neurodevelopmental Disorders

Authors : Giuseppe Massimo Bernava, Marco Leo, Pierluigi Carcagnì, Cosimo Distante

Published in: Image Analysis and Processing. ICIAP 2022 Workshops

Publisher: Springer International Publishing

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Abstract

Non-invasive solutions (no sensors nor markers) appear the most appealing for assessment of body movements and facial dynamics in order to predict Neurodevelopmental disorders (NDD) even in the first days of life. To this aim, recent advances in machine learning applied could be effectively exploited on visual data framing the children, but they suffer from the scarcity of annotated data for training the algorithms. In order to fill this gap, in this paper, a semi-automatic tool specifically designed for labelling videos of children in cribs is introduced. It consists of a Graphical User Interface allowing to select: 1) videos, or static images, to be processed and 2) the desired annotation goal achieved by state-of-the-art deep learning based neural architectures.

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previous chapter Deep-Learning Architectures for Placenta Vessel Segmentation in TTTS Fetoscopic Images
next chapter Some Ethical Remarks on Deep Learning-Based Movements Monitoring for Preterm Infants: Green AI or Red AI?
Footnotes
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Metadata
Title
An Advanced Tool for Semi-automatic Annotation for Early Screening of Neurodevelopmental Disorders
Authors
Giuseppe Massimo Bernava
Marco Leo
Pierluigi Carcagnì
Cosimo Distante
Copyright Year
2022
Book
Image Analysis and Processing. ICIAP 2022 Workshops

Print ISBN: 978-3-031-13323-7

Electronic ISBN: 978-3-031-13324-4

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-031-13324-4_14

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