Machine learning models reproducibility and validation for MR images recognition

Ekaterina Kondrateva; Polina Belozerova; Maxim Sharaev; Evgeny Burnaev; Alexander Bernstein; Irina Samotaeva

doi:10.1117/12.2559525

31 January 2020 Machine learning models reproducibility and validation for MR images recognition

Ekaterina Kondrateva, Polina Belozerova, Maxim Sharaev, Evgeny Burnaev, Alexander Bernstein, Irina Samotaeva

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114330Z (2020) https://doi.org/10.1117/12.2559525
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

In the present work, we introduce a data processing and analysis pipeline, which ensures the reproducibility of machine learning models chosen for MR image recognition. The proposed pipeline is applied to solve the binary classification problems: epilepsy and depression diagnostics based on vectorized features from MR images. This model is then assessed in terms of classification performance, robustness and reliability of the results, including predictive accuracy on unseen data. The classification performance achieved with our approach compares favorably to ones reported in the literature, where usually no thorough model evaluation is performed.

Citation Download Citation

Ekaterina Kondrateva, Polina Belozerova, Maxim Sharaev, Evgeny Burnaev, Alexander Bernstein, and Irina Samotaeva "Machine learning models reproducibility and validation for MR images recognition", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114330Z (31 January 2020); https://doi.org/10.1117/12.2559525

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