Uncertainty Quantification in COVID-19 Detection Using Evidential Deep Learning
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Abstract
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.
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SciScore for 10.1101/2022.05.29.22275732: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources We used the MADGRAD as our optimizer as it outperformed other tested alternatives [10]. MADGRADsuggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the …
SciScore for 10.1101/2022.05.29.22275732: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources We used the MADGRAD as our optimizer as it outperformed other tested alternatives [10]. MADGRADsuggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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