BiaPy: accessible deep learning on bioimages Articles uri icon

authors

  • FRANCO BARRANCO, DANIEL
  • ANDRES SAN ROMAN, JESUS A.
  • HIDALGO CENALMOR, IVAN
  • BACKOVA, LENKA
  • GONZALEZ MARFIL, AITOR
  • CAPORAL, CLEMENT
  • CHESSEL, ANATOLE
  • GOMEZ GALVEZ, PEDRO
  • ESCUDERO, LUIS M.
  • WEI, DONGLAI
  • MUÑOZ BARRUTIA, MARIA ARRATE
  • ARGANDA CARRERAS, IGNACIO

publication date

  • June 2025

start page

  • 1124

end page

  • 1126

issue

  • 6

volume

  • 22

International Standard Serial Number (ISSN)

  • 1548-7091

Electronic International Standard Serial Number (EISSN)

  • 1548-7105

abstract

  • Bioimage analysis is a cornerstone of modern life sciences, powering discoveries and insights derived from biological image data. Deep learning has become an invaluable tool for analyzing microscopy datasets, and its application is increasingly widespread in biomedical research1. However, its prerequisite for high-level programming skills has often acted as a barrier, limiting accessibility for researchers without a specific computational background.

subjects

  • Biology and Biomedicine
  • Medicine

keywords

  • computational platforms and environments; image processing; machine learning; software