{
  "abstract": "This study characterizes spatial, spectral, relational, and contextual representations for binary gastric histopathological image classification using FLIGENT-Net. The framework combines an ImageNet-pretrained convolutional backbone, patch-level fast Fourier transform (FFT) descriptors, graph convolution over a cosine-similarity graph, and a Transformer Encoder. Experiments use the 160-by-160 GasHisSDB subset, a persisted stratified 60%/20%/20% partition, and one training run per configuration with seed 42. In the nine completed configurations of the revised evaluation, FLIGENT-Net achieved 97.31% accuracy, 96.57% precision, 96.61% recall, 96.59% F1-score for the Abnormal class, and 0.9964 AUC-ROC. An additional native-grid FFT control yielded 97.43% accuracy and 96.74% F1 under an alternative spectral protocol that preserved the 160-by-160 sampling grid while changing the local FFT window sizes and spectral discretization. The conventional CNN baseline (ResNet18) obtained the highest accuracy and F1-score among these completed runs, at 98.51% and 98.13%, respectively. These results do not establish a predictive advantage for the complete multi-domain architecture; rather, they characterize how the added representation domains affect predictive behavior, graph topology, computational cost, and attribution response under a common protocol. The study contributes a reproducible integration of the four representation domains and a controlled empirical characterization of their combinations. Transformer attention visualizations describe information allocation among contextualized nodes, while a common input-gradient deletion protocol examines attribution behavior. Together, these analyses identify the behavior and limitations of the implemented fusion strategy and provide a basis for its further development. Performance estimates are conditional on the available patch-level split and should not be interpreted as patient-, slide-, or source-image-independent generalization; run-to-run variability is also not estimated because one fixed seed is used per configuration.",
  "abstract_shown": true,
  "affiliations": [
    "Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico",
    "Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Unidad Zacatenco, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zayra Reyes-Vera",
      "orcid": "0009-0004-4515-8140"
    },
    {
      "affs": [
        0
      ],
      "name": "Jesús Yaljá Montiel-Pérez",
      "orcid": "0000-0002-0214-1080"
    },
    {
      "affs": [
        1
      ],
      "name": "José Luis López-Bonilla",
      "orcid": "0000-0003-3147-7162"
    },
    {
      "affs": [
        0
      ],
      "name": "Elsa Rubio-Espino",
      "orcid": "0000-0002-3217-908X"
    },
    {
      "affs": [
        0
      ],
      "name": "Juan Humberto Sossa-Azuela",
      "orcid": "0000-0002-0521-4898"
    },
    {
      "affs": [
        0
      ],
      "name": "Víctor Hugo Ponce-Ponce",
      "orcid": "0000-0001-5699-0478"
    },
    {
      "affs": [
        0
      ],
      "name": "Osvaldo Espinosa-Sosa",
      "orcid": "0009-0009-3687-3501"
    },
    {
      "affs": [
        1
      ],
      "name": "Sergio Vidal-Beltrán",
      "orcid": "0000-0003-3412-9630"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/asgn9mdhjedzw.bib",
  "cited_by": 0,
  "doi": "10.3390/math14213867",
  "fulltext_url": null,
  "id": "asgn9mdhjedzw",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/21/3867",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7223696022",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/asgn9mdhjedzw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14213867",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/21/3867"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Characterizing Multi-Domain Representations for Gastric Histopathological Image Classification",
  "title_text": "Characterizing Multi-Domain Representations for Gastric Histopathological Image Classification",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/asgn9mdhjedzw",
  "venue": "Mathematics",
  "versions": []
}