Ensemble based in transfer learning for cytological classification in pleural fluid - Scorecard - MDSpire

Ensemble based in transfer learning for cytological classification in pleural fluid

  • By

  • Frida López-Córdova

  • Hugo Vega-Huerta

  • Gisella Luisa Elena Maquen-Niño

  • Jaime Cáceres-Pizarro

  • Ciro Rodriguez

  • David Calderón

  • Juan Gamarra-Moreno

  • Percy De-la-Cruz-VdV

  • Luis Guerra-Grados

  • Santiago Moquillaza-Henríquez

  • Oscar Benito-Pacheco

  • Ivan Adrianzén-Olano

  • Mario Chauca

  • June 10, 2026

  • 0 min

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Clinical Scorecard: Transfer Learning Utilizing Ensemble Methods for the Classification of Cytological Images in Pleural Fluid

At a Glance

CategoryDetail
Condition
Key Mechanisms
Target PopulationPatients with pleural effusion requiring cytological examination for diagnosis.
Care Setting

Key Highlights

  • Clarify the role of 300% data augmentation in enhancing model performance.

Guideline-Based Recommendations

Diagnosis

    Management

    • Further tests such as CT scans or biopsies should be performed if there is clinical suspicion of MPE with negative cytology.

    Monitoring & Follow-up

      Risks

        Patient & Prescribing Data

        Automated diagnostic support can improve access to timely cancer diagnosis, but may have limitations in accuracy.

        Clinical Best Practices

        • Consider utilizing specific AI tools like TensorFlow or PyTorch for ensemble model implementation.

        Related Resources & Content

        Original Source(s)

        Ensemble based in transfer learning for cytological classification in pleural fluid

        • by Frida López-Córdova, Hugo Vega-Huerta, Gisella Luisa Elena Maquen-Niño, Jaime Cáceres-Pizarro, Ciro Rodriguez, David Calderón, Juan Gamarra-Moreno, Percy De-la-Cruz-VdV, Luis Guerra-Grados, Santiago Moquillaza-Henríquez, Oscar Benito-Pacheco, Ivan Adrianzén-Olano, Mario Chauca

        • June 10, 2026

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