Artículo
Invariant representations in deep learning for optoacoustic imaging
Fecha de publicación:
05/2023
Editorial:
American Institute of Physics
Revista:
Review of Scientific Instruments
ISSN:
0034-6748
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Image reconstruction in optoacoustic tomography (OAT) is a trending learning task highly dependent on measured physical magnitudes present at sensing time. A large number of different settings and also the presence of uncertainties or partial knowledge of parameters can lead to reconstruction algorithms that are specifically tailored and designed to a particular configuration, which could not be the one that will ultimately be faced in a final practical situation. Being able to learn reconstruction algorithms that are robust to different environments (e.g., the different OAT image reconstruction settings) or invariant to such environments is highly valuable because it allows us to focus on what truly matters for the application at hand and discard what are considered spurious features. In this work, we explore the use of deep learning algorithms based on learning invariant and robust representations for the OAT inverse problem. In particular, we consider the application of the ANDMask scheme due to its easy adaptation to the OAT problem. Numerical experiments are conducted showing that when out-of-distribution generalization (against variations in parameters such as the location of the sensors) is imposed, there is no degradation of the performance and, in some cases, it is even possible to achieve improvements with respect to standard deep learning approaches where invariance robustness is not explicitly considered.
Palabras clave:
OAT
,
Out of Distribution
,
Invariant
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Articulos(CSC)
Articulos de CENTRO DE SIMULACION COMPUTACIONAL P/APLIC. TECNOLOGICAS
Articulos de CENTRO DE SIMULACION COMPUTACIONAL P/APLIC. TECNOLOGICAS
Citación
Vera, Matías Alejandro; González, Martín Germán; Rey Vega, Leonardo Javier; Invariant representations in deep learning for optoacoustic imaging; American Institute of Physics; Review of Scientific Instruments; 94; 5; 5-2023; 1-11
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