Handling limited datasets with neural networks in medical applications: A small-data approach

Handling limited datasets with neural networks in medical applications: A small-data approach. Shaikhina, T. & Khovanova, N. A. Artificial Intelligence in Medicine, 75:51–63, January, 2017.

Paper doi abstract bibtex

Methods: In order to address the sporadic ﬂuctuations and validation issues that appear in regression NNs trained on small datasets, the method of multiple runs and surrogate data analysis were proposed in this work. The approach was compared to the state-of-the-art ensemble NNs; the effect of dataset size on NN performance was also investigated. Results: The proposed framework was applied for the prediction of compressive strength (CS) of femoral trabecular bone in patients suffering from severe osteoarthritis. The NN model was able to estimate the CS of osteoarthritic trabecular bone from its structural and biological properties with a standard error of 0.85 MPa. When evaluated on independent test samples, the NN achieved accuracy of 98.3%, outperforming an ensemble NN model by 11%. We reproduce this result on CS data of another porous solid (concrete) and demonstrate that the proposed framework allows for an NN modelled with as few as 56 samples to generalise on 300 independent test samples with 86.5% accuracy, which is comparable to the performance of an NN developed with 18 times larger dataset (1030 samples). Conclusion: The signiﬁcance of this work is two-fold: the practical application allows for non-destructive prediction of bone fracture risk, while the novel methodology extends beyond the task considered in this study and provides a general framework for application of regression NNs to medical problems characterised by limited dataset sizes.

@article{shaikhina_handling_2017,
	title = {Handling limited datasets with neural networks in medical applications: {A} small-data approach},
	volume = {75},
	issn = {09333657},
	shorttitle = {Handling limited datasets with neural networks in medical applications},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0933365716301749},
	doi = {10.1016/j.artmed.2016.12.003},
	abstract = {Methods: In order to address the sporadic ﬂuctuations and validation issues that appear in regression NNs trained on small datasets, the method of multiple runs and surrogate data analysis were proposed in this work. The approach was compared to the state-of-the-art ensemble NNs; the effect of dataset size on NN performance was also investigated.
Results: The proposed framework was applied for the prediction of compressive strength (CS) of femoral trabecular bone in patients suffering from severe osteoarthritis. The NN model was able to estimate the CS of osteoarthritic trabecular bone from its structural and biological properties with a standard error of 0.85 MPa. When evaluated on independent test samples, the NN achieved accuracy of 98.3\%, outperforming an ensemble NN model by 11\%. We reproduce this result on CS data of another porous solid (concrete) and demonstrate that the proposed framework allows for an NN modelled with as few as 56 samples to generalise on 300 independent test samples with 86.5\% accuracy, which is comparable to the performance of an NN developed with 18 times larger dataset (1030 samples).
Conclusion: The signiﬁcance of this work is two-fold: the practical application allows for non-destructive prediction of bone fracture risk, while the novel methodology extends beyond the task considered in this study and provides a general framework for application of regression NNs to medical problems characterised by limited dataset sizes.},
	language = {en},
	urldate = {2021-05-08},
	journal = {Artificial Intelligence in Medicine},
	author = {Shaikhina, Torgyn and Khovanova, Natalia A.},
	month = jan,
	year = {2017},
	pages = {51--63},
}

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Results: The proposed framework was applied for the prediction of compressive strength (CS) of femoral trabecular bone in patients suffering from severe osteoarthritis. The NN model was able to estimate the CS of osteoarthritic trabecular bone from its structural and biological properties with a standard error of 0.85 MPa. When evaluated on independent test samples, the NN achieved accuracy of 98.3%, outperforming an ensemble NN model by 11%. We reproduce this result on CS data of another porous solid (concrete) and demonstrate that the proposed framework allows for an NN modelled with as few as 56 samples to generalise on 300 independent test samples with 86.5% accuracy, which is comparable to the performance of an NN developed with 18 times larger dataset (1030 samples). Conclusion: The signiﬁcance of this work is two-fold: the practical application allows for non-destructive prediction of bone fracture risk, while the novel methodology extends beyond the task considered in this study and provides a general framework for application of regression NNs to medical problems characterised by limited dataset sizes.","language":"en","urldate":"2021-05-08","journal":"Artificial Intelligence in Medicine","author":[{"propositions":[],"lastnames":["Shaikhina"],"firstnames":["Torgyn"],"suffixes":[]},{"propositions":[],"lastnames":["Khovanova"],"firstnames":["Natalia","A."],"suffixes":[]}],"month":"January","year":"2017","pages":"51–63","bibtex":"@article{shaikhina_handling_2017,\n\ttitle = {Handling limited datasets with neural networks in medical applications: {A} small-data approach},\n\tvolume = {75},\n\tissn = {09333657},\n\tshorttitle = {Handling limited datasets with neural networks in medical applications},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0933365716301749},\n\tdoi = {10.1016/j.artmed.2016.12.003},\n\tabstract = {Methods: In order to address the sporadic ﬂuctuations and validation issues that appear in regression NNs trained on small datasets, the method of multiple runs and surrogate data analysis were proposed in this work. The approach was compared to the state-of-the-art ensemble NNs; the effect of dataset size on NN performance was also investigated.\nResults: The proposed framework was applied for the prediction of compressive strength (CS) of femoral trabecular bone in patients suffering from severe osteoarthritis. The NN model was able to estimate the CS of osteoarthritic trabecular bone from its structural and biological properties with a standard error of 0.85 MPa. When evaluated on independent test samples, the NN achieved accuracy of 98.3\\%, outperforming an ensemble NN model by 11\\%. We reproduce this result on CS data of another porous solid (concrete) and demonstrate that the proposed framework allows for an NN modelled with as few as 56 samples to generalise on 300 independent test samples with 86.5\\% accuracy, which is comparable to the performance of an NN developed with 18 times larger dataset (1030 samples).\nConclusion: The signiﬁcance of this work is two-fold: the practical application allows for non-destructive prediction of bone fracture risk, while the novel methodology extends beyond the task considered in this study and provides a general framework for application of regression NNs to medical problems characterised by limited dataset sizes.},\n\tlanguage = {en},\n\turldate = {2021-05-08},\n\tjournal = {Artificial Intelligence in Medicine},\n\tauthor = {Shaikhina, Torgyn and Khovanova, Natalia A.},\n\tmonth = jan,\n\tyear = {2017},\n\tpages = {51--63},\n}\n\n","author_short":["Shaikhina, T.","Khovanova, N. 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