Medical imaging is an important tool for the diagnosis and localization of disease. Over the past decade, collections of medical images have grown rapidly, and open repositories such as The Cancer Imaging Archive and Imaging Data Commons have democratized access to this vast imaging data. Computational tools such as machine learning (ML) and artificial intelligence (AI) have emerged as an effective and viable option for rapid analysis of this imaging data. Many algorithms have been developed for different kinds of image analysis. These include classification, segmentation, and localization, to name a few. However, the development of the algorithm and training of the required ML model is only one piece of the larger ML/AI puzzle.
Cost-efficient and high-performance deployment of the model is also vital. Additionally, for a model to be of any use at scale, it must be deployed for inference in a reliable, scalable environment.
In this post, we

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