Medical Imaging Datasets, Artifacts, and their Living Review

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a summary:Data collections play an important role in medical photography research, but issues such as stickers, shortcuts and descriptive data are often ignored. This interest in attention may harm the generalization of algorithms, and thus negatively affects the results of the patient. While current medical literature reviews focus on automated learning methods (ML), with a focus on data collections only on specific applications, these reviews remain fixed – they are published once and not updated after that. This fails to calculate emerging evidence, such as biases, shortcuts and additional explanations that other researchers may contribute after the publication of the data collection. We refer to these newly discovered results of data groups as research artifacts. To process this gap, we suggest a live review that continuously tracks general data collections and research antiques associated with them through multiple medical imaging applications. Our approach includes a framework for living review to monitor the artifacts of data documents, and a SQL database to visualize the quotation relationship between the artifact and the data group. Finally, we discuss the main considerations for creating medical photography data collections, reviewing best practices for data suspension, discussing the importance of shortcomings and demographic diversity, and emphasizing the importance of managing data groups throughout their entire life cycle. Our experimental offer is available to the audience at this URL http.