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Deep learning, due to its unprecedented success in tasks such as imageclassification, has emerged as a new tool in image reconstruction withpotential to change the field. In this paper we demonstrate a crucialphenomenon: deep learning typically yields unstablemethods for imagereconstruction. The instabilities usually occur in several forms: (1) tiny,almost undetectable perturbations, both in the image and sampling domain, mayresult in severe artefacts in the reconstruction, (2) a small structuralchange, for example a tumour, may not be captured in the reconstructed imageand (3) (a counterintuitive type of instability) more samples may yield poorerperformance. Our new stability test with algorithms and easy to use softwaredetects the instability phenomena. The test is aimed at researchers to testtheir networks for instabilities and for government agencies, such as the Foodand Drug Administration (FDA), to secure safe use of deep learning methods.

On instabilities of deep learning in image reconstruction - Does AI come at a cost?