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Habilitación de la Anotación Autónoma de Datos en Imágenes de Mamografía: Un Enfoque de Aprendizaje por Refuerzo con

Leonardo C da Cruz1, Cesar A Sierra-Franco2, Alberto Raposo2

  • 1Tecgraf Institute and Department of Informatics, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil. leonardocardia@gmail.com.

Journal of imaging informatics in medicine
|January 6, 2026
PubMed
Resumen

Este estudio presenta un novedoso enfoque de aprendizaje por refuerzo profundo (DRL), "Try a Little More" (TLM), para automatizar la generación de anotaciones para la detección de objetos. TLM reduce significativamente el esfuerzo humano y mejora la calidad de los datos de entrenamiento para modelos de IA.

Palabras clave:
AnotacionesCuadro delimitadorRed Q profundaAprendizaje por refuerzo profundoMamografíaReducir esfuerzos

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