Abstract：
:Deep learning-based systems can achieve a diagnostic performance comparable to physicians in a variety of medical use cases including the diagnosis of diabetic retinopathy. To be useful in clinical practice, it is necessary to have well calibrated measures of the uncertainty with which these systems report their decisions. However, deep neural networks (DNNs) are being often overconfident in their predictions, and are not amenable to a straightforward probabilistic treatment. Here, we describe an intuitive framework based on test-time data augmentation for quantifying the diagnostic uncertainty of a state-of-the-art DNN for diagnosing diabetic retinopathy. We show that the derived measure of uncertainty is well-calibrated and that experienced physicians likewise find cases with uncertain diagnosis difficult to evaluate. This paves the way for an integrated treatment of uncertainty in DNN-based diagnostic systems.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Ayhan MS,Kühlewein L,Aliyeva G,Inhoffen W,Ziemssen F,Berens Pdoi
10.1016/j.media.2020.101724subject
Has Abstractpub_date
2020-08-01 00:00:00pages
101724eissn
1361-8415issn
1361-8423pii
S1361-8415(20)30088-8journal_volume
64pub_type
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