FAIR Skin Lesion Classification Workflows using Transfer Learning

In the domain of skin lesion classification using computer-aided diagnosis, machine learning approaches found in the literature are reported to be highly effective. However, state-of-the-art findings can prove challenging to reimplement due to inconsistencies and ambiguities in recorded methodologies. These ambiguities reduce the velocity at which future research advancements can be achieved. This paper proposes a machine learning configuration capture method that obtains a complete and faithful descriptor of a machine learning workflow. This descriptor is serialised into a sharable file format, enabling sub-sequent research to reimplement a cited model to a high degree of accuracy. Following this configuration capture, reproducing input data sources is an essential step in the faithful reimple-mentation of baseline models. A centralised data sourcing tool for the automated acquisition of highly cited skin lesion datasets from various sources is also delivered. The work contributes a standardised approach in creating reproducible and sharable machine learning-based workflows, enabling accelerated machine learning research in the domain of skin lesion classification.