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In recent years, Clinical Decision Support Systems (CDSSs) have been developed to support the complex decisions that take place in clinical practice. Despite their rapid evolution, they face many challenges such as speed, interoperability, and precision requirements. Our research explores a collection of tools and technologies to improve the current deep learning CDDSs. Our work is focused on medical imaging for pulmonary diseases as a study case. This paper discusses the methodology, first results, and techniques implemented during the research, such as the database creation, training strategies, and disease prediction with deep learning ensembles. All these techniques represent an improvement of the current systems and are highly generalizable to other image techniques and diseases.

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Evolving Deep Learning Techniques for Disease Diagnosis Support Using Medical Imaging

  • 会议论文

  • 发表日期：

    2025年

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  • Lara Visuña,
  • Javier Garcia-Blas,
  • Jesus Carretero

摘要

In recent years, Clinical Decision Support Systems (CDSSs) have been developed to support the complex decisions that take place in clinical practice. Despite their rapid evolution, they face many challenges such as speed, interoperability, and precision requirements. Our research explores a collection of tools and technologies to improve the current deep learning CDDSs. Our work is focused on medical imaging for pulmonary diseases as a study case. This paper discusses the methodology, first results, and techniques implemented during the research, such as the database creation, training strategies, and disease prediction with deep learning ensembles. All these techniques represent an improvement of the current systems and are highly generalizable to other image techniques and diseases.