Handling limited datasets with neural networks in medical applications: A small-data approach
作者：
Highlights：
• A novel framework enables NN analysis in medical applications involving small datasets.
• An accurate model for trabecular bone strength estimation in severe osteoarthritis is developed.
• Model enables non-invasive patient-specific prediction of hip fracture risk.
• Method of multiple runs mitigates sporadic fluctuations in NN performance due to small data.
• Surrogate data test is used to account for random effects due to small test data.
摘要
•A novel framework enables NN analysis in medical applications involving small datasets.•An accurate model for trabecular bone strength estimation in severe osteoarthritis is developed.•Model enables non-invasive patient-specific prediction of hip fracture risk.•Method of multiple runs mitigates sporadic fluctuations in NN performance due to small data.•Surrogate data test is used to account for random effects due to small test data.
论文关键词：Predictive modelling,Small data,Regression neural networks,Osteoarthritis,Compressive strength,Trabecular bone
论文评审过程：Received 12 May 2016, Revised 21 November 2016, Accepted 28 December 2016, Available online 2 January 2017, Version of Record 12 January 2017.
论文官网地址：https://doi.org/10.1016/j.artmed.2016.12.003
