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LBDNet interlaboratory comparison for the dicentric chromosome assay by digitized image analysis applying weighted robust statistical methods.

Jorge Ernesto González MesaDiego Alem GlisonFabio Andrés Chaves-CamposFernando Ortíz MoralesLuisa Valle BourrouetMelissa Abarca RamírezValentina VerdejoMarina Di GiorgioAnalía RadlMaría Rosa TajaMayra DemingeAna Rada-TarifaErika Lafuente-AlvarezFabiana Farias de LimaSuy HwangMariana Esposito MendesTania Mandina-CardosoGabriela Muñoz-VelasteguiYolanda Citlali Guerrero-CarbajalCarolina Arceo MaldonadoNorma MonjagataSara Aguilar-CoronelMarco Espinoza-ZevallosAida Falcon de VargasMaria Vittoria Di TomasoBret HolladayOmar García LimaWilner Martínez-López
Published in: International journal of radiation biology (2024)
The results underscore the significance of performing interlaboratory comparison exercises that involve digitized and electronically transmitted images, even when analyzing non-irradiated samples. In situations where the participating laboratories possess different levels of proficiency, it may prove essential to employ weighted robust algorithms to achieve precise outcomes.
Keyphrases
  • deep learning
  • magnetic resonance
  • machine learning
  • network analysis
  • high throughput
  • copy number
  • magnetic resonance imaging
  • dna methylation
  • adipose tissue
  • resistance training