Devices enabled by artificial intelligence (AI) and machine learning (ML) are being introduced for clinical use at an accelerating pace. In a dynamic clinical environment, these devices may encounter conditions different from those they were developed for. The statistical data mismatch between training/initial testing and production is often referred to as data drift. Detecting and quantifying data drift is significant for ensuring that AI model performs as expected in clinical environments. A drift detector signals when a corrective action is needed if the performance changes. In this study, we investigate how a change in the performance of an AI model due to data drift can be detected and quantified using a cumulative sum (CUSUM) control chart. To study the properties of CUSUM, we first simulate different scenarios that change the performance of an AI model. We simulate a sudden change in the mean of the performance metric at a change-point (change day) in time. The task is to quickly detect the change while providing few false-alarms before the change-point, which may be caused by the statistical variation of the performance metric over time. Subsequently, we simulate data drift by denoising the Emory Breast Imaging Dataset (EMBED) after a pre-defined change-point. We detect the change-point by studying the pre- and post-change specificity of a mammographic CAD algorithm. Our results indicate that with the appropriate choice of parameters, CUSUM is able to quickly detect relatively small drifts with a small number of false-positive alarms.
The cost of data-movement is one of the fundamental issues with modern compute systems processing Big Data workloads. One approach to move the computation closer to data is to equip the storage or memory devices with processing power. The notion of moving computation to data is known as Near Data Processing (NDP). In this work, we re-examine the idea of reducing the data movement by processing data directly in the storage devices. We evaluate ASTOR, a compute framework on an Active Storage platform, which incorporates a software stack and a dedicated multi-core processor for in-storage processing. ASTOR utilizes the processing power of storage devices by using an array of Active Drive™ devices to significantly reduce the bandwidth requirement on the network. We evaluate the performance and scalability of ASTOR for distributed processing of Big Data workloads. We conclude by discussing a comparative study of other existing data-centric approaches.
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