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Integration of nested cross-validation, automated hyperparameter optimization, high-performance computing to reduce

Paul Calle1, Averi Bates1, Justin C Reynolds1

  • 1School of Computer Science, University of Oklahoma, Norman, 73019, OK, USA.

Computer Methods and Programs in Biomedicine
|September 14, 2025
PubMed
Summary

NACHOS, a new framework using Nested Cross-Validation and Automated Hyperparameter Optimization, reduces and quantifies performance variance in deep learning models for medical imaging. This enhances trustworthiness for real-world deployment.

Keywords:
Deep learningMedical imagingNested cross-validationPerformance evaluation

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Area of Science:

  • Medical imaging
  • Deep learning
  • Computational science

Background:

  • Deep learning model performance benchmarking in medical imaging suffers from variability and bias, hindering real-world trust.
  • Current methods using fixed test sets inadequately capture performance metric variance.
  • This necessitates robust evaluation frameworks for reliable medical AI deployment.

Purpose of the Study:

  • Introduce NACHOS (Nested and Automated Cross-validation and Hyperparameter Optimization using Supercomputing) to reduce and quantify performance metric variance.
  • Develop a framework for trustworthy deep learning model evaluation in medical imaging.
  • Enhance the reliability of AI tools in clinical settings.

Main Methods:

  • NACHOS integrates Nested Cross-Validation (NCV) and Automated Hyperparameter Optimization (AHPO) within a parallelized high-performance computing (HPC) framework.
  • Demonstrated on chest X-ray and Optical Coherence Tomography (OCT) datasets.
  • Introduced DACHOS (Deployment with Automated Cross-validation and Hyperparameter Optimization using Supercomputing) for final model building on full datasets.

Main Results:

  • NCV is crucial for quantifying and reducing estimation variance.
  • AHPO ensures consistent hyperparameter optimization across test folds.
  • HPC guarantees the computational feasibility of the proposed framework.

Conclusions:

  • NACHOS and DACHOS offer a scalable, reproducible, and trustworthy framework for deep learning model evaluation and deployment in medical imaging.
  • The open-source codebase is publicly available to promote adoption and further research.
  • This framework addresses critical needs for reliable AI in healthcare.