Background and objectives: The use of deep learning for preterm infant's movement monitoring has the potential to support clinicians in early recognizing motor and behavioural disorders. The development of deep learning algorithms is, however, hampered by the lack of publicly available annotated datasets.Methods: To mitigate the issue, this paper presents a Generative Adversarial Network-based framework to generate images of preterm infants in a given pose. The framework consists of a bibranch encoder and a conditional Generative Adversarial Network, to generate a rough image and a refined version of it, respectively.Results: Evaluation was performed on the Moving INfants In RGB-D dataset which has 12.000 depth frames from 12 preterm infants. A low Frechet inception distance (142.9) and an inception score (2.8) close to that of real-image distribution (2.6) are obtained. The results achieved show the potentiality of the framework in generating realistic depth images of preterm infants in a given pose.Conclusions: Pursuing research on the generation of new data may enable researchers to propose increasingly advanced and effective deep learning-based monitoring systems.

Generating depth images of preterm infants in given poses using GANs

Moccia, Sara
2022-01-01

Abstract

Background and objectives: The use of deep learning for preterm infant's movement monitoring has the potential to support clinicians in early recognizing motor and behavioural disorders. The development of deep learning algorithms is, however, hampered by the lack of publicly available annotated datasets.Methods: To mitigate the issue, this paper presents a Generative Adversarial Network-based framework to generate images of preterm infants in a given pose. The framework consists of a bibranch encoder and a conditional Generative Adversarial Network, to generate a rough image and a refined version of it, respectively.Results: Evaluation was performed on the Moving INfants In RGB-D dataset which has 12.000 depth frames from 12 preterm infants. A low Frechet inception distance (142.9) and an inception score (2.8) close to that of real-image distribution (2.6) are obtained. The results achieved show the potentiality of the framework in generating realistic depth images of preterm infants in a given pose.Conclusions: Pursuing research on the generation of new data may enable researchers to propose increasingly advanced and effective deep learning-based monitoring systems.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/828902
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