<?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>0120-4157</journal-id>
<journal-title><![CDATA[Biomédica]]></journal-title>
<abbrev-journal-title><![CDATA[Biomed.]]></abbrev-journal-title>
<issn>0120-4157</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Salud]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-41572025000600083</article-id>
<article-id pub-id-type="doi">10.7705/biomedica.7899</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Clasificación de la expresión del receptor 2 del factor de crecimiento epidérmico humano en tejido mamario canceroso mediante inteligencia artificial]]></article-title>
<article-title xml:lang="en"><![CDATA[Classification of human epidermal growth factor receptor 2 expression in cancerous breast tissue through artificial intelligence]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Villota]]></surname>
<given-names><![CDATA[Leidy Verónica]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lasso]]></surname>
<given-names><![CDATA[Jessica Julieth]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Muñoz]]></surname>
<given-names><![CDATA[Elvia Noélia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vargas]]></surname>
<given-names><![CDATA[Rubiel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad del Cauca Grupo de Investigación en Sistemas Dinámicos, Instrumentación y Control ]]></institution>
<addr-line><![CDATA[Popayán ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Unidad de Diagnóstico en Patología SAS  ]]></institution>
<addr-line><![CDATA[Popayán ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<volume>45</volume>
<fpage>83</fpage>
<lpage>102</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-41572025000600083&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-41572025000600083&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-41572025000600083&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Introducción. El análisis histológico y molecular del tejido mamario es clave para el diagnóstico, el pronóstico y el tratamiento del cáncer de mama. Entre los biomarcadores evaluados, se destacan los receptores de progesterona, los de estrógeno y el receptor 2 del factor de crecimiento epidérmico humano (HER2). La sobreexpresión de HER2 indica un subtipo agresivo de cáncer de mama, aunque permite el uso de terapias dirigidas que mejoran la tasa de supervivencia. No obstante, su evaluación enfrenta desafíos, desde la calidad de las muestras hasta la variabilidad en la interpretación. El College of American Pathologists clasifica la sobreexpresión de HER2 en cuatro categorías, pero la variabilidad en la expresión cercana al 10 % puede generar confusión.  Objetivo. Presentar una técnica basada en la inteligencia artificial para clasificar células con sobreexpresión de HER2 en las placas histológicas.  Materiales y métodos. Se aplicó la metodología Cross-Industry Standard Process for Data Mining (CRISP-DM) en muestras de 89 pacientes de la Unidad de Diagnóstico en Patología, abarcando los cuatro niveles de HER2. Se utilizaron redes neuronales y modelos de Vision Transformer (ViT) afinados mediante transferencia de aprendizaje. Además, se evaluó la facilidad de uso y, finalmente, la eficiencia del software presentado.  Resultados. Con el modelo ViT-B/16, se obtuvo una exactitud del 90,65 % en la clasificación, mientras que la herramienta evaluada generó un grado aceptable de satisfacción con su aplicación clínica.  Conclusión. La inteligencia artificial demostró gran precisión y concordancia en la clasificación del HER2, redujo la variabilidad diagnóstica y mejoró la objetividad, aunque aún se requiere optimizar la eficiencia del procesamiento.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Introduction. Histological and molecular analysis of breast tissue is essential for the diagnosis, prognosis, and treatment of breast cancer. Key biomarkers include progesterone and estrogen receptors, as well as the human epidermal growth factor receptor 2 (HER2). HER2 overexpression indicates an aggressive subtype of breast cancer but enables targeted therapies that improve survival rates. However, its evaluation faces challenges, ranging from sample quality to interpretation variability. The College of American Pathologists classifies HER2 overexpression into four categories, but variations around the 10% expression threshold can lead to misinterpretations.  Objective. To present an automated technique for classifying HER2-overexpressing cells in histological slides.  Materials and methods. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was applied using samples of 89 patients from the Unidad de Diagnóstico en Patología, covering all four HER2 expression levels. Deep learning techniques were employed, leveraging neural networks and vision transformer models through transfer learning. Additionally, a usability evaluation was conducted on the final version of the software.  Results. The ViT-B/16 model achieved a classification accuracy of 90,65%, while the tool was evaluated with an acceptable level of satisfaction in its clinical application.  Conclusion. Artificial intelligence demonstrated high accuracy and consistency in HER2 classification, reducing diagnostic variability and improving objectivity. However, further optimization of processing efficiency is required for broader applicability.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[neoplasias de la mama]]></kwd>
<kwd lng="es"><![CDATA[inmunohistoquímica]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="en"><![CDATA[Breast cancer]]></kwd>
<kwd lng="en"><![CDATA[immunohistochemistry]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<label>1</label><nlm-citation citation-type="">
<collab>American Cancer Society</collab>
<source><![CDATA[What is cancer | Cancer basics]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B2">
<label>2</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bórquez]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Pezoa]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Salinas]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Torres]]></surname>
<given-names><![CDATA[CE]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Uncertainty estimation in the classification of histopathological images with HER2 overexpression using Monte Carlo Dropout]]></article-title>
<source><![CDATA[Biomed Signal Process Control]]></source>
<year>2023</year>
<numero>85</numero>
<issue>85</issue>
<page-range>104864</page-range></nlm-citation>
</ref>
<ref id="B3">
<label>3</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Swain]]></surname>
<given-names><![CDATA[SM]]></given-names>
</name>
<name>
<surname><![CDATA[Shastry]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Hamilton]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Targeting HER2-positive breast cancer: Advances and future directions]]></article-title>
<source><![CDATA[Nat Rev Drug Discov]]></source>
<year>2023</year>
<numero>22</numero>
<issue>22</issue>
<page-range>101&#8209;26</page-range></nlm-citation>
</ref>
<ref id="B4">
<label>4</label><nlm-citation citation-type="">
<collab>American Cancer Society</collab>
<source><![CDATA[Breast cancer HER2 status | What is HER2 status?]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B5">
<label>5</label><nlm-citation citation-type="">
<collab>American Society of Clinical Oncology</collab>
<source><![CDATA[Breast cancer]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B6">
<label>6</label><nlm-citation citation-type="">
<collab>International Agency for Research on Cancer</collab>
<source><![CDATA[Cancer today]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B7">
<label>7</label><nlm-citation citation-type="">
<collab>Departamento Administrativo Nacional de Estadística</collab>
<source><![CDATA[Defunciones no fetales 2020]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B8">
<label>8</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zheng]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Liang]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[LMBNet: Lightweight multiple branch network for recognition of HER2 expression levels]]></article-title>
<source><![CDATA[Proc Comput Sci]]></source>
<year>2023</year>
<numero>222</numero>
<issue>222</issue>
<page-range>197&#8209;206</page-range></nlm-citation>
</ref>
<ref id="B9">
<label>9</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Córdova]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Muñoz]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Olivares]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Minonzio]]></surname>
<given-names><![CDATA[JG]]></given-names>
</name>
<name>
<surname><![CDATA[Lozano]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[González]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry]]></article-title>
<source><![CDATA[Oncol Lett]]></source>
<year>2023</year>
<numero>25</numero>
<issue>25</issue>
<page-range>44</page-range></nlm-citation>
</ref>
<ref id="B10">
<label>10</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Shao]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[W]]></given-names>
</name>
<name>
<surname><![CDATA[Liang]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[HER2&#8209;ResNet: A HER2 classification method based on deep residual network]]></article-title>
<source><![CDATA[Technol Health Care]]></source>
<year>2022</year>
<volume>30</volume>
<numero>^s1</numero>
<issue>^s1</issue>
<supplement>1</supplement>
<page-range>215&#8209;24</page-range></nlm-citation>
</ref>
<ref id="B11">
<label>11</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mirimoghaddam]]></surname>
<given-names><![CDATA[MM]]></given-names>
</name>
<name>
<surname><![CDATA[Majidpour]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Pashaei]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Arabalibeik]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Samizadeh]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Roshan]]></surname>
<given-names><![CDATA[NM]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[HER2GAN: Overcome the scarcity of HER2 breast cancer dataset based on transfer learning and GAN Model]]></article-title>
<source><![CDATA[Clin Breast Cancer]]></source>
<year>2024</year>
<numero>24</numero>
<issue>24</issue>
<page-range>53&#8209;64</page-range></nlm-citation>
</ref>
<ref id="B12">
<label>12</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Selcuk]]></surname>
<given-names><![CDATA[SY]]></given-names>
</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Bai]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Aydin]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Automated HER2 scoring in breast cancer images using deep learning and pyramid sampling]]></article-title>
<source><![CDATA[BME Front]]></source>
<year>2024</year>
<numero>5</numero>
<issue>5</issue>
<page-range>00&#8209;48</page-range></nlm-citation>
</ref>
<ref id="B13">
<label>13</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chauhan]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Ghanshala]]></surname>
<given-names><![CDATA[KK]]></given-names>
</name>
<name>
<surname><![CDATA[Joshi]]></surname>
<given-names><![CDATA[RC]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Convolutional Neural Network (CNN) for image detection and recognition]]></article-title>
<source><![CDATA[First International Conference on Secure Cyber Computing and Communication (ICSCCC)]]></source>
<year>2018</year>
<page-range>278&#8209;82</page-range><publisher-loc><![CDATA[Jalandhar, India ]]></publisher-loc>
<publisher-name><![CDATA[IEEE]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B14">
<label>14</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Vaswani]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Shazeer]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Parmar]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Uszkoreit]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Jones]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Gómez]]></surname>
<given-names><![CDATA[AN]]></given-names>
</name>
</person-group>
<source><![CDATA[Attention is all you need. Preprint. arXiv]]></source>
<year>2023</year>
</nlm-citation>
</ref>
<ref id="B15">
<label>15</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dosovitskiy]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Beyer]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Kolesnikov]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Weissenborn]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Zhai]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Unterthiner]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
</person-group>
<source><![CDATA[An image is worth 16 × 16 words: Transformers for image recognition at scale. Preprint. arXiv]]></source>
<year>2021</year>
</nlm-citation>
</ref>
<ref id="B16">
<label>16</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[Z]]></given-names>
</name>
<name>
<surname><![CDATA[Lin]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Cao]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Hu]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Wei]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[Z]]></given-names>
</name>
</person-group>
<source><![CDATA[Swin transformer: Hierarchical visión transformer using shifted windows. Preprint. arXiv]]></source>
<year>2021</year>
<page-range>14030</page-range></nlm-citation>
</ref>
<ref id="B17">
<label>17</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Khan]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Naseer]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Hayat]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Zamir]]></surname>
<given-names><![CDATA[SW]]></given-names>
</name>
<name>
<surname><![CDATA[Khan]]></surname>
<given-names><![CDATA[FS]]></given-names>
</name>
<name>
<surname><![CDATA[Shah]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Transformers in vision: A survey]]></article-title>
<source><![CDATA[ACM Comput Surv]]></source>
<year>2022</year>
<volume>54</volume>
<numero>10s</numero>
<issue>10s</issue>
<page-range>1&#8209;41</page-range></nlm-citation>
</ref>
<ref id="B18">
<label>18</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chollet]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
</person-group>
<source><![CDATA[Deep learning with Python]]></source>
<year>2021</year>
<edition>Second edition</edition>
<page-range>1&#8209;502</page-range><publisher-loc><![CDATA[New York, NY ]]></publisher-loc>
<publisher-name><![CDATA[Simon and Schuster]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B19">
<label>19</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Theckedath]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Sedamkar]]></surname>
<given-names><![CDATA[RR]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Detecting affect states using VGG16, ResNet50 and SE&#8209;ResNet50 networks]]></article-title>
<source><![CDATA[SN Comput Sci]]></source>
<year>2020</year>
<numero>1</numero>
<issue>1</issue>
<page-range>79</page-range></nlm-citation>
</ref>
<ref id="B20">
<label>20</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Özalt&#305;n]]></surname>
<given-names><![CDATA[Ö]]></given-names>
</name>
<name>
<surname><![CDATA[Yeniay]]></surname>
<given-names><![CDATA[Ö]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Detection of monkeypox disease from skin lesion images using mobilenetv2 architecture]]></article-title>
<source><![CDATA[Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics]]></source>
<year>2023</year>
<numero>72</numero>
<issue>72</issue>
<page-range>482&#8209;99</page-range></nlm-citation>
</ref>
<ref id="B21">
<label>21</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tan]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Le]]></surname>
<given-names><![CDATA[QV]]></given-names>
</name>
</person-group>
<source><![CDATA[EfficientNet: Rethinking model scaling for convolutional neural networks. Preprint. arXiv]]></source>
<year>2020</year>
<page-range>1905.11946</page-range></nlm-citation>
</ref>
<ref id="B22">
<label>22</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Fang]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Sun]]></surname>
<given-names><![CDATA[Q]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Cao]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[EVA-02: A visual representation for Neon Genesis]]></article-title>
<source><![CDATA[Image Vis Comput]]></source>
<year>2024</year>
<numero>149</numero>
<issue>149</issue>
<page-range>105&#8209;71</page-range></nlm-citation>
</ref>
<ref id="B23">
<label>23</label><nlm-citation citation-type="book">
<collab>Stanford Vision Lab</collab>
<source><![CDATA[Princeton University]]></source>
<year>2025</year>
<publisher-name><![CDATA[ImageNet]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B24">
<label>24</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shaga Devan]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Kestler]]></surname>
<given-names><![CDATA[HA]]></given-names>
</name>
<name>
<surname><![CDATA[Read]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Walther]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Weighted average ensemble-based semantic segmentation in biological electron microscopy images]]></article-title>
<source><![CDATA[Histochem Cell Biol]]></source>
<year>2022</year>
<numero>158</numero>
<issue>158</issue>
<page-range>447&#8209;62</page-range></nlm-citation>
</ref>
<ref id="B25">
<label>25</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[ZH]]></given-names>
</name>
</person-group>
<source><![CDATA[Ensemble methods: Foundations and algorithms]]></source>
<year>2012</year>
<edition>First edition</edition>
<page-range>1&#8209;236</page-range><publisher-loc><![CDATA[Boca Ratón, FL ]]></publisher-loc>
<publisher-name><![CDATA[Chapman &amp; Hall/CRC]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B26">
<label>26</label><nlm-citation citation-type="">
<collab>Gradio</collab>
<source><![CDATA[Gradio App]]></source>
<year>2025</year>
</nlm-citation>
</ref>
<ref id="B27">
<label>27</label><nlm-citation citation-type="">
<collab>International Organization for Standardization (ISO)</collab>
<source><![CDATA[ISO/IEC 25022:2016 - Systems and software engineering - Systems and software quality requirements and evaluation (SQuaRE) - Measurement of quality in use]]></source>
<year>2025</year>
</nlm-citation>
</ref>
<ref id="B28">
<label>28</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Brooke]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<source><![CDATA[SUS - A quick and dirty usability scale]]></source>
<year>2025</year>
</nlm-citation>
</ref>
</ref-list>
</back>
</article>
