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Using Noisy Evaluation to Accelerate Parameter Optimization of Medical Image Segmentation Ensembles

Author

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  • János Tóth

    (Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, 4028 Debrecen, Hungary
    HUN-REN–UD Equations, Functions, Curves and Their Applications Research Group, 4032 Debrecen, Hungary)

  • Henrietta Tomán

    (Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, 4028 Debrecen, Hungary)

  • Gabriella Hajdu

    (Institute of Mathematics and Basic Science, Hungarian University of Agricultural and Life Sciences, 2100 Gödöllő, Hungary)

  • András Hajdu

    (Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, 4028 Debrecen, Hungary)

Abstract

An important concern with regard to the ensembles of algorithms is that using the individually optimal parameter settings of the members does not necessarily maximize the performance of the ensemble itself. In this paper, we propose a novel evaluation method for simulated annealing that combines dataset sampling and image downscaling to accelerate the parameter optimization of medical image segmentation ensembles. The scaling levels and sample sizes required to maintain the convergence of the search are theoretically determined by adapting previous results for simulated annealing with imprecise energy measurements. To demonstrate the efficiency of the proposed method, we optimize the parameters of an ensemble for lung segmentation in CT scans. Our experimental results show that the proposed method can maintain the solution quality of the base method with significantly lower runtime. In our problem, optimization with simulated annealing yielded an F 1 score of 0.9397 and an associated M C C of 0.7757. Our proposed method maintained the solution quality with an F 1 score of 0.9395 and M C C of 0.7755 while exhibiting a 42.01% reduction in runtime. It was also shown that the proposed method is more efficient than simulated annealing with only sampling-based evaluation when the dataset size is below a problem-specific threshold.

Suggested Citation

  • János Tóth & Henrietta Tomán & Gabriella Hajdu & András Hajdu, 2023. "Using Noisy Evaluation to Accelerate Parameter Optimization of Medical Image Segmentation Ensembles," Mathematics, MDPI, vol. 11(18), pages 1-17, September.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3992-:d:1243773
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    References listed on IDEAS

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    1. West, David & Mangiameli, Paul & Rampal, Rohit & West, Vivian, 2005. "Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application," European Journal of Operational Research, Elsevier, vol. 162(2), pages 532-551, April.
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