Creational and Structural Patterns in a Flexible Machine Learning Framework for Medical Ultrasound Diagnostics

Abstract

The impact of machine learning in medicine has arguably lagged behind its commercial counterparts. This may be attributable to the generally slower pace and higher costs associated with clinical applications, but also present are the conflicting constraints and requirements of learning from data in a highly regulated industry that introduce levels of complexity unique to the medical space. Because of this, the balance between innovation and controlled development is challenging. Adding to this are the multiple modalities found in most clinical applications where applying traditional machine learning preprocessing and cross-validation techniques can be precarious. This work presents the novel use of creational and structural design patterns in a generalized software framework intended to alleviate some of those difficulties. Designed to be a configurable pipeline to not only support the experimentation and development of diagnostic machine learning algorithms, but also to support the transition of those algorithms into production level systems in a composed manner. The resulting framework provides the foundation for developing unique tools by both novice and expert data scientists.