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The possibility to use widespread and simple chest X-ray (CXR) imaging forearly screening of COVID-19 patients is attracting much interest from both theclinical and the AI community. In this study we provide insights and also raisewarnings on what is reasonable to expect by applying deep-learning to COVIDclassification of CXR images. We provide a methodological guide and criticalreading of an extensive set of statistical results that can be obtained usingcurrently available datasets. In particular, we take the challenge posed bycurrent small size COVID data and show how significant can be the biasintroduced by transfer-learning using larger public non-COVID CXR datasets. Wealso contribute by providing results on a medium size COVID CXR dataset, justcollected by one of the major emergency hospitals in Northern Italy during thepeak of the COVID pandemic. These novel data allow us to contribute to validatethe generalization capacity of preliminary results circulating in thescientific community. Our conclusions shed some light into the possibility toeffectively discriminate COVID using CXR.

Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data