title: Selection bias in the reported performances of AD classification pipelines.
abstract: The last decade has seen a great proliferation of supervised learning pipelines for individual diagnosis and prognosis in Alzheimer's disease. As more pipelines are developed and evaluated in the search for greater performance, only those results that are relatively impressive will be selected for publication. We present an empirical study to evaluate the potential for optimistic bias in classification performance results as a result of this selection. This is achieved using a novel, resampling-based experiment design that effectively simulates the optimisation of pipeline specifications by individuals or collectives of researchers using cross validation with limited data. Our findings indicate that bias can plausibly account for an appreciable fraction (often greater than half) of the apparent performance improvement associated with the pipeline optimisation, particularly in small samples. We discuss the consistency of our findings with patterns observed in the literature and consider strategies for bias reduction and mitigation.
authors.affiliation: Translational Imaging Group, Centre for Medical Image Computing University College London, London, UK.
authors.forename: Alex F
authors.initials: AF
authors.lastname: Mendelson
authors.affiliation: Translational Imaging Group, Centre for Medical Image Computing University College London, London, UK.
authors.forename: Maria A
authors.initials: MA
authors.lastname: Zuluaga
authors.affiliation: Translational Imaging Group, Centre for Medical Image Computing University College London, London, UK.
authors.forename: Marco
authors.initials: M
authors.lastname: Lorenzi
authors.affiliation: Institute of Nuclear Medicine, University College London, London, UK; Centre for Medical Radiation Physics, University of Wollongong, NSW, Australia.
authors.forename: Brian F
authors.initials: BF
authors.lastname: Hutton
authors.affiliation: Translational Imaging Group, Centre for Medical Image Computing University College London, London, UK; Dementia Research Centre, University College London, UK.
authors.forename: Sébastien
authors.initials: S
authors.lastname: Ourselin
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000145:classification* / Q000000981:diagnostic imaging / Q000453:epidemiology*; D015982:Bias; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D015203:Reproducibility of Results; D015983:Selection Bias
keywords: ADNI; Alzheimer's disease; Classification; Cross validation; Overfitting; Selection bias