Methods for Creating Reproducible Machine Learning Pipelines for Skin Lesion Classification
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Our work on a standardized approach for creating reproducible and sharable machine learning-based workflows for skin lesion classification as presented by David Walshe at the 2021 IEEE EMBS International Conference on Biomedical and Health Informatics. It combines a centralized data sourcing tool for the automated acquisition of highly cited skin lesion datasets from various online repositories. It provides a machine learning configuration-capture method that obtains a complete and faithful descriptor of a machine learning workflow. This descriptor is serialized into a sharable file format, enabling subsequent research to reimplement a cited model to a high degree of accuracy. The intent is to reduce the work associated with reimplementing state-of-the-art findings that arise from inconsistencies and ambiguities in recorded methodologies, increasing the velocity at which future research advancements can be achieved enabling a faithful reimplementation of baseline models.