Background
Deep neural networks are showing impressive results in different medical image classification tasks. However, for real-world applications, there is a need to estimate the network’s uncertainty together with its prediction.
Objective
In this review, we investigate in what form uncertainty estimation has been applied to the task of medical image classification. We also investigate which metrics are used to describe the effectiveness of the applied uncertainty estimation
Methods
Google Scholar, PubMed, IEEE Xplore, and ScienceDirect were screened for peer-reviewed studies, published between 2016 and 2021, that deal with uncertainty estimation in medical image classification. The search terms “uncertainty,” “uncertainty estimation,” “network calibration,” and “out-of-distribution detection” were used in combination with the terms “medical images,” “medical image analysis,” and “medical image classification.”
Results
A total of 22 papers were chosen for detailed analysis through the systematic review process. This paper provides a table for a systematic comparison of the included works with respect to the applied method for estimating the uncertainty.
Conclusions
The applied methods for estimating uncertainties are diverse, but the sampling-based methods Monte-Carlo Dropout and Deep Ensembles are used most frequently. We concluded that future works can investigate the benefits of uncertainty estimation in collaborative settings of artificial intelligence systems and human experts.
International Registered Report Identifier (IRRID)
RR2-10.2196/11936
The Russian writer Ivan Sergeevič Turgenev (1818–83), who lived in Western Europe (Germany, England, and France) during the second half of his life, is considered the most important mediator between Russia and Europe in the nineteenth century due to his wide and intensive contacts in East and West. The paper aims to trace Turgenev’s literary and cultural contacts using the epistemological model of the net and current methods of analyzing social networks on a quantitative and qualitative level. In concrete terms, Turgenev’s postal relations from a single year (from June 1868 to May 1869) are presented and evaluated in tabular form and as GEPHI graphs. Beyond the purely quantitative network visualization and viewing, the attempt is made to provide a cultural weighting of the exchange, especially of Turgenev’s German contacts. The network-specific weighting of these contacts results in a different emphasis than usual in Turgenev research, which focuses on Turgenev’s contacts with important German writers. The qualitative analysis carried out on the basis of the visualization shows that Turgenev’s contacts with literary celebrities such as Theodor Storm, Berthold Auerbach, and Paul Heyse proved to be weak ties. In contrast, his relationship with the little-known literary figure Ludwig Pietsch deserves to be called a strong tie. Turgenev’s position and agency in the network can be described with Burt as a “broker” attitude.
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