Methods for Creating Reproducible Machine Learning Pipelines for Skin Lesion Classification

Abstract

In the domain of skin lesion classification using computer-aided diagnosis (CAD), machine learning (ML) 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 abstract proposes an ML configuration capture method that obtains a complete and faithful descriptor of an ML workflow. This descriptor is serialised into a sharable file format, enabling subsequent 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 reimplementation of baseline models. This abstract also delivers a centralised data sourcing tool for the automated acquisition of highly cited skin lesion datasets from various sources. The work contributes a standardised approach in creating reproducible and sharable ML-based workflows, enabling accelerated machine learning research in the domain of skin lesion classification.

ResearchGate

Recommended citation: David Walshe, Ruairi O'Reilly (2021). “Methods for Creating Reproducible Machine Learning Pipelines for Skin Lesion Classification.” 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI).