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CellViT++: Segmentación y clasificación celular eficiente y adaptable mediante modelos fundacionales

Fabian Hörst1, Moritz Rempe1, Helmut Becker2

  • 1Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Essen, 45131, Germany; Cancer Research Center Cologne Essen (CCCE), West German Cancer Center Essen, University Hospital Essen (AöR), Essen, 45131, Germany; Department of Physics, TU Dortmund University, Dortmund, 44227, Germany.

Computer methods and programs in biomedicine
|January 23, 2026
PubMed
Resumen
Este resumen es generado por máquina.

CellViT++ ofrece un marco de aprendizaje profundo eficiente en cuanto a datos para la segmentación celular en patología digital. Este modelo ligero se adapta rápidamente a nuevos tipos de células con datos mínimos, reduciendo los costos computacionales y el tiempo de anotación.

Palabras clave:
Inteligencia artificialCélulasPatología digitalModelos fundacionalesSegmentación

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Sus antecedentes:

  • Los modelos de aprendizaje profundo para la segmentación celular requieren datos anotados extensos y son computacionalmente costosos.
  • Los métodos existentes carecen de adaptabilidad a nuevos tipos de células, lo que crea cuellos de botella en los flujos de trabajo de investigación y clínicos.
  • Se introduce CellViT++ para abordar estas limitaciones en la segmentación y clasificación celular.

Conclusiones:

  • CellViT++ es un marco robusto, eficiente y de código abierto que desacopla la segmentación de la clasificación en patología computacional.
  • La adaptabilidad del marco a nuevos tipos de células con datos mínimos y la generación automatizada de conjuntos de datos reducen significativamente la necesidad de anotación experta.
  • CellViT++ sirve como una herramienta fundamental para acelerar la investigación, mejorar los diagnósticos y permitir un análisis más profundo de cohortes.