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Exploring the values underlying machine learning research in medical image analysis.

John S H Baxter1, Roy Eagleson2

  • 1Laboratoire Traitement du Signal et de l'Image (LTSI, INSERM UMR 1099), Université de Rennes, Rennes, France.

Medical Image Analysis
|February 28, 2025
PubMed
Summary
This summary is machine-generated.

This study explores the philosophical underpinnings of machine learning in medical image analysis. It connects scientific values to technical decisions in deep learning for improved research practices.

Keywords:
Intermediate representationsMachine learningPhilosophy of scienceResearch values

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Area of Science:

  • Medical image analysis
  • Philosophy of science
  • Machine learning

Background:

  • Machine learning, particularly deep artificial neural networks, is vital for modern medical image analysis.
  • Effective use requires understanding underlying motivations and philosophical foundations, not just algorithms.
  • Non-empirical values significantly influence scientific research, including in medical imaging.

Purpose of the Study:

  • Introduce values specific to medical image analysis.
  • Characterize the end-to-end vs. separable learning spectrum in machine learning.
  • Develop a method to rigorously connect values to technical decisions in this field.

Main Methods:

  • Philosophical analysis of scientific values.
  • Characterization of the end-to-end vs. separable learning spectrum.
  • Development of a structured method for value-decision linkage.

Main Results:

  • Established a framework for understanding values in medical image analysis.
  • Detailed the technical spectrum of learning approaches.
  • Provided a method to connect values to specific technical choices.

Conclusions:

  • Philosophy of science can clarify and improve medical image analysis research.
  • Understanding the influence of values on technical decisions is crucial.
  • Rigorous connection of values to machine learning practices enhances research quality.