<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1684-1859</journal-id>
<journal-title><![CDATA[Revista Cubana de Informática Médica]]></journal-title>
<abbrev-journal-title><![CDATA[RCIM]]></abbrev-journal-title>
<issn>1684-1859</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Ciencias Médicas de La Habana]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1684-18592022000100015</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Deep Learning aplicado en imágenes fotoacústicas para la Identificación del cáncer de seno]]></article-title>
<article-title xml:lang="en"><![CDATA[Deep Learning Applied in Photoacoustic Images for the Identification of Breast Cancer]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ruíz]]></surname>
<given-names><![CDATA[Estefanía]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Domínguez]]></surname>
<given-names><![CDATA[Jesús Emmanuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Nacional de Astrofísica, Óptica y Electrónica Ciencias y Tecnologías Biomédicas ]]></institution>
<addr-line><![CDATA[ Puebla]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<volume>14</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1684-18592022000100015&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1684-18592022000100015&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1684-18592022000100015&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La Imagen Fotoacústica (PAI por sus siglas en inglés), es una modalidad de imagen híbrida que fusiona la iluminación óptica y la detección por ultrasonido. Debido a que los métodos de imágenes ópticas puras no pueden mantener una alta resolución, la capacidad de lograr imágenes de contraste óptico de alta resolución en tejidos biológicos hace que la fotoacústica (PA por sus siglas en inglés) sea una técnica prometedora para varias aplicaciones de imágenes clínicas. En la actualidad el Aprendizaje Profundo (Deep Learning) tiene el enfoque más reciente en métodos basados en la PAI, donde existe una gran cantidad de aplicaciones en análisis de imágenes, en especial en el área del campo biomédico, como lo es la adquisición, segmentación y reconstrucciones de imágenes de tomografía computarizada. Esta revisión describe las últimas investigaciones en PAI y un análisis sobre las técnicas y métodos basados en Deep Learning, aplicado en diferentes modalidades para el diagnóstico de cáncer de seno.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Photoacoustic Imaging (PAI) is a hybrid imaging modality that combines optical illumination and ultrasound detection. Because pure optical imaging methods cannot maintain high resolution, the ability to achieve high resolution optical contrast images in biological tissues makes Photoacoustic (PA) a promising technique for various clinical imaging applications. At present, Deep Learning has the most recent approach of methods based on PAI where there are a large number of applications in image analysis especially in the area of &#8203;&#8203;the biomedical field, such as acquisition, segmentation and reconstructions of computed tomography imaging. This review describes the latest research in PAI and an analysis of the techniques and methods based on Deep Learning applied in different modalities for the diagnosis of breast cancer.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[imágenes fotoacústicas]]></kwd>
<kwd lng="es"><![CDATA[tomografía fotoacústica]]></kwd>
<kwd lng="es"><![CDATA[deep learning]]></kwd>
<kwd lng="es"><![CDATA[machine learning]]></kwd>
<kwd lng="es"><![CDATA[cáncer de seno]]></kwd>
<kwd lng="es"><![CDATA[cáncer de mama]]></kwd>
<kwd lng="es"><![CDATA[reconstrucción de Imágenes]]></kwd>
<kwd lng="en"><![CDATA[photoacoustic images]]></kwd>
<kwd lng="en"><![CDATA[photoacoustic tomography]]></kwd>
<kwd lng="en"><![CDATA[deep learning]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[breast cancer]]></kwd>
<kwd lng="en"><![CDATA[image reconstruction]]></kwd>
</kwd-group>
</article-meta>
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