Abstract:
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Diagnostic medical imaging has been transformed in recent years by deep learning models trained on large amounts of patient data. Though there has been much progress and enthusiasm, these approaches have shed new light on classic issues in confounding, study design, and model validation. In this talk, we will highlight key challenges in the deep learning model validation pipeline and provide examples of subtle ways that these challenges may present themselves at different stages of model development.
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