<?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>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462023000400909</article-id>
<article-id pub-id-type="doi">10.13053/cys-27-4-4777</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[PumaMedNet-CXR: An Explainable Generative Artificial Intelligence for the Analysis and Classification of Chest X-Ray Images]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Minutti-Martínez]]></surname>
<given-names><![CDATA[Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Escalante-Ramírez]]></surname>
<given-names><![CDATA[Boris]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Olveres-Montiel]]></surname>
<given-names><![CDATA[Jimena]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional Autónoma de México Centro de Estudios en Computación Avanzada ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional Autónoma de México Laboratorio Avanzado de Procesamiento de Imágenes ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>27</volume>
<numero>4</numero>
<fpage>909</fpage>
<lpage>920</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462023000400909&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462023000400909&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462023000400909&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: In this paper, we introduce PumaMedNet-CXR, a generative AI designed for medical image classification, with a specific emphasis on Chest X-ray (CXR) images. The model effectively corrects common defects in CXR images, offers improved explainability, enabling a deeper understanding of its decision-making process. By analyzing its latent space, we can identify and mitigate biases, ensuring a more reliable and transparent model. Notably, PumaMedNet-CXR achieves comparable performance to larger pre-trained models through transfer learning, making it a promising tool for medical image analysis. The model&#8217;s highly efficient autoencoder-based architecture, along with its explainability and bias mitigation capabilities, contribute to its significant potential in advancing medical image understanding and analysis.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Medical image analysis]]></kwd>
<kwd lng="en"><![CDATA[autoencoder]]></kwd>
<kwd lng="en"><![CDATA[explainable artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[chest X-Ray]]></kwd>
</kwd-group>
</article-meta>
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