Deep Learning in Medical Post-Processing
A well-known manufacturer of medical image post-processing applications and long-time Method Park customer in the sector of medical technology asked Method Park engineers to support its evaluation of new technology trends in the algorithmic image processing domain. The customer aimed at increasing the quality of its products, while maintaining the same level of product suitability for daily use.
Main goal was the evaluation of opportunities in machine learning – particularly Deep Learning – and, if applicable, their integration into existing medical devices. Method Park supported this customer on his journey from the prototype, to the integration of robust Deep Learning-based algorithms.
Duration of the project
about 1.5 years
2 .net developers
First, an established Open Source Deep Learning library was introduced into the international multi-team project setup. The library was adapted to meet the project’s technological requirements and license rights, under consideration of the customer’s existing processes and relevant norms and standards in the regulated medical environment.
Principles of Software Craftsmanship were considered and implemented compliant to industry standards. Additionally, all tasks were bound to numerous requirements of the medical and regulatory environment, including creation and maintenance of comprehensive technical documentation.
Method Park ensured compliance to requirements in terms of code efficiency, execution times and consumption of software resources on customer-specific hardware configurations. In the course of development, Method Park worked closely together with clinical experts. Method Park engineers were contact persons for all questions by the customer’s technical experts who used the new implemented functionalities. Additionally, Method Park was in constant dialogue with medical algorithmic image processing scientists.
Method Park engineers integrated the new machine learning technology into the existing comprehensive code base of the customer project. Method Park’s work included preventive measures against unwanted side effects on algorithmic results due to changes in central components.
Introduction of an Open Source Deep Learning library
Consideration of Software Craftsmanship rules
Integration of latest clinical and scientific expert knowledge
With the support of Method Park, the customer successfully implemented the new technological trend Deep Learning with the benefit of increased software quality in medical imaging. That way the customer strengthened his position as a technological leader.
Early application of the technological trend Deep Learning
Standard-compliant integration of new technologies
Increased software Quality
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