The interpretation of deep learning based analysis of medical images: an examination of methodological and practical challenges using chest X-ray data

With the increase in availability of annotated X-ray image data, there has been an accompanying and consequent increase in research on machine learning based, and particularly deep learning based, X-ray image analysis. A major problem with this body of work lies in how newly proposed algorithms are evaluated. Usually, comparative analysis is reduced to the presentation of a single metric, often the area under the receiver operating characteristic (AUROC), which does not provide much clinical value or insight, and thus fails to communicate the applicability of proposed models. In the present paper we address this limitation of previous work by presenting a thorough analysis of a state of the art learning approach, and hence illuminate various weaknesses of similar algorithms in the literature, which have not yet been fully acknowledged and appreciated. Our analysis is performed on the ChestX-ray14 dataset which has 14 lung disease labels and metainfo such as patient age, gender, and the relative X-ray direction. We examine the diagnostic significance of different metrics used in the literature including those proposed by the International Medical Device Regulators Forum, and present qualitative assessment of spatial information learnt by the model. We show that models that have very similar AUROCs can exhibit widely differing clinical applicability. As a result, our work demonstrates the importance of detailed reporting and analysis of performance of machine learning approaches in this field, which is crucial both for progress in the field and the adoption of such models in practice.