2019
Balki, Indranil; Amirabadi, Afsaneh; Levman, Jacob; Martel, Anne L; Emersic, Ziga; Meden, Blaz; Garcia-Pedrero, Angel; Ramirez, Saul C; Kong, Dehan; Moody, Alan R; Tyrrell, Pascal N
Sample-Size Determination Methodologies for Machine Learning in Medical Imaging Research: A Systematic Review Journal Article
In: Canadian Association of Radiologists Journal, vol. 70, no. 4, pp. 344–353, 2019, ISSN: 0846-5371.
Abstract | Links | BibTeX | Tags: Machine learning, Medical Imaging, Radiology, Sample size
@article{Balki2019b,
title = {Sample-Size Determination Methodologies for Machine Learning in Medical Imaging Research: A Systematic Review},
author = {Indranil Balki and Afsaneh Amirabadi and Jacob Levman and Anne L Martel and Ziga Emersic and Blaz Meden and Angel Garcia-Pedrero and Saul C Ramirez and Dehan Kong and Alan R Moody and Pascal N Tyrrell},
url = {https://doi.org/10.1016/j.carj.2019.06.002 http://journals.sagepub.com/doi/10.1016/j.carj.2019.06.002},
doi = {10.1016/j.carj.2019.06.002},
issn = {0846-5371},
year = {2019},
date = {2019-11-01},
journal = {Canadian Association of Radiologists Journal},
volume = {70},
number = {4},
pages = {344–353},
publisher = {Elsevier Inc.},
abstract = {Purpose: The required training sample size for a particular machine learning (ML) model applied to medical imaging data is often unknown. The purpose of this study was to provide a descriptive review of current sample-size determination methodologies in ML applied to medical imaging and to propose recommendations for future work in the field. Methods: We conducted a systematic literature search of articles using Medline and Embase with keywords including “machine learning,” “image,” and “sample size.” The search included articles published between 1946 and 2018. Data regarding the ML task, sample size, and train-test pipeline were collected. Results: A total of 167 articles were identified, of which 22 were included for qualitative analysis. There were only 4 studies that discussed sample-size determination methodologies, and 18 that tested the effect of sample size on model performance as part of an exploratory analysis. The observed methods could be categorized as pre hoc model-based approaches, which relied on features of the algorithm, or post hoc curve-fitting approaches requiring empirical testing to model and extrapolate algorithm performance as a function of sample size. Between studies, we observed great variability in performance testing procedures used for curve-fitting, model assessment methods, and reporting of confidence in sample sizes. Conclusions: Our study highlights the scarcity of research in training set size determination methodologies applied to ML in medical imaging, emphasizes the need to standardize current reporting practices, and guides future work in development and streamlining of pre hoc and post hoc sample size approaches.},
keywords = {Machine learning, Medical Imaging, Radiology, Sample size},
pubstate = {published},
tppubtype = {article}
}
Purpose: The required training sample size for a particular machine learning (ML) model applied to medical imaging data is often unknown. The purpose of this study was to provide a descriptive review of current sample-size determination methodologies in ML applied to medical imaging and to propose recommendations for future work in the field. Methods: We conducted a systematic literature search of articles using Medline and Embase with keywords including “machine learning,” “image,” and “sample size.” The search included articles published between 1946 and 2018. Data regarding the ML task, sample size, and train-test pipeline were collected. Results: A total of 167 articles were identified, of which 22 were included for qualitative analysis. There were only 4 studies that discussed sample-size determination methodologies, and 18 that tested the effect of sample size on model performance as part of an exploratory analysis. The observed methods could be categorized as pre hoc model-based approaches, which relied on features of the algorithm, or post hoc curve-fitting approaches requiring empirical testing to model and extrapolate algorithm performance as a function of sample size. Between studies, we observed great variability in performance testing procedures used for curve-fitting, model assessment methods, and reporting of confidence in sample sizes. Conclusions: Our study highlights the scarcity of research in training set size determination methodologies applied to ML in medical imaging, emphasizes the need to standardize current reporting practices, and guides future work in development and streamlining of pre hoc and post hoc sample size approaches.