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Open-Source Biomedical Image Analysis Models: A Meta-Analysis and Continuous Survey.

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This summary is machine-generated.

Open-source software is vital for biomedical image analysis, but novel machine learning algorithms present new challenges for maintaining openness. A meta-analysis revealed that only a fraction of publications share all necessary components for reproducibility.

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Area of Science:

  • Biomedical Image Analysis
  • Open-Source Software
  • Machine Learning

Background:

  • Open-source research software is crucial for advancing biomedical image analysis.
  • Machine learning (ML) offers significant improvements to image analysis algorithms.
  • Novel ML algorithms introduce new requirements for open-source practices.

Purpose of the Study:

  • To assess the extent to which open-source biomedical image analysis models meet the requirements for reproducibility.
  • To analyze the availability of essential components (papers, code, data, parameters) in published models.

Main Methods:

  • Collected 50 biomedical image analysis models.
  • Performed a meta-analysis of associated publications, source code, datasets, and trained model parameters.
  • Evaluated the completeness of shared resources for each model.

Main Results:

  • Identified positive trends in the openness of research software.
  • Found that a limited proportion of publications make all necessary elements available.
  • Highlighted a gap between the potential of open-source ML and its current implementation in biomedical imaging.

Conclusions:

  • While open-source biomedical image analysis is growing, full reproducibility remains a challenge.
  • Efforts are needed to ensure comprehensive sharing of code, data, and parameters for ML-driven tools.
  • Improving openness will accelerate innovation and collaboration in the field.