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In the light of the COVID-19 pandemic, deep learning methods have been widelyinvestigated in detecting COVID-19 from chest X-rays. However, a more pragmaticapproach to applying AI methods to a medical diagnosis is designing a frameworkthat facilitates human-machine interaction and expert decision making. Studieshave shown that categorization can play an essential rule in acceleratingreal-world decision making. Inspired by descriptive document clustering, wepropose a domain-independent explanatory clustering framework to groupcontextually related instances and support radiologists' decision making. Whilemost descriptive clustering approaches employ domain-specific characteristicsto form meaningful clusters, we focus on model-level explanation as a moregeneral-purpose element of every learning process to achieve clusterhomogeneity. We employ DeepSHAP to generate homogeneous clusters in terms ofdisease severity and describe the clusters using favorable and unfavorablesaliency maps, which visualize the class discriminating regions of an image.These human-interpretable maps complement radiologist knowledge to investigatethe whole cluster at once. Besides, as part of this study, we evaluate a modelbased on VGG-19, which can identify COVID and pneumonia cases with a positivepredictive value of 95% and 97%, respectively, comparable to the recentexplainable approaches for COVID diagnosis.

ViDi: Descriptive Visual Data Clustering as Radiologist Assistant in COVID-19 Streamline Diagnostic