Uncertainty Quantification in COVID-19 Detection Using Evidential Deep Learning

Considering the immense pace of developments in deep learning (DL), its applications in medicine are relatively limited. One main issue that hinders the utilization of DL in the medical practice workflow is its reliability. A radiologist interpreting an image can easily say “I don’t know”, while a DL model is forced to output a result. Evidential deep learning (EDL) is one of the methods for uncertainty quantification (UQ). In this work, we aimed to use EDL to express model uncertainty in detecting COVID-19. We used SIIM-FISABIO-RSNA COVID-19 chest x-ray dataset and trained a model to diagnose typical COVID-19 pneumonia. When applied to a separate test set, it yielded an accuracy of 88% with median uncertainty scores of 0.25 and 0.07 for normal and typical COVID-19 images, respectively. Moreover, the model labeled unseen indeterminate and atypical COVID-19 x-rays with median uncertainties of 0.32 and 0.35, respectively. Our model’s performance was superior to the exact model trained with conventional approach of DL (i.e., using the cross-entropy loss), which is not able to express the uncertainty level. Overall, this study demonstrates applicability of UQ in disease detection that could facilitate the use of DL in practice by increasing its reliability. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used (or will use) ONLY openly available human data that were originally located at: https://www.kaggle.com/competitions/siim-covid19-detection/overview I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced are available online at: https://www.kaggle.com/competitions/siim-covid19-detection/data