Evaluating reproducibility of AI algorithms in digital pathology with DAPPER.

ABSTRACT: Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to digital pathology is natural, with the promise of aiding in routine reporting and standardizing results across trials. Deep learning features inferred from digital pathology scans can improve validity and robustness of current clinico-pathological features, up to identifying novel histological patterns, e.g., from tumor infiltrating lymphocytes. In this study, we examine the issue of evaluating accuracy of predictive models from deep learning features in digital pathology, as an hallmark of reproducibility. We introduce the DAPPER framework for validation based on a rigorous Data Analysis Plan derived from the FDA's MAQC project, designed to

SUBMITTER: Bizzego A 

PROVIDER: S-EPMC6467397 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature