Physician Friendly Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) today has infiltrated almost all fields, helping catch patterns and make interesting conclusions from data. The surge in AI over the years can be attributed to two main facts: Increase in computation power of newer systems and the availability of data, both of which serve as seeds for building good prediction models. Medicine has slowly but steadily adopted AI over the years. Yet, traditional heuristic approaches and experience of physicians and doctors is heavily relied upon to this date. This thesis proposes two machine learning tools that can help doctors, physicians and medical researchers with their diagnosis and treatment procedures. Proposal 1 discusses Automatic Machine Learning (AutoML), which is a tool that helps automate the process of ML model building and fine-tuning, taking away the onus of fine tuning model parameters from the programmer. The resistance towards adoption of ML in the medical community stems from the idea that the tools and knowledge are only accessible to highly trained ML experts. This proposal is an attempt at breaking this age-old perception by proposing Auto-ML as a tool to build good ML models. The experiment done to substantiate this claim is to have a graduate student with sufficient experience in ML, manually build and fine-tune ML models on two publicly available cardiovascular disease prediction data-sets over a month and compare the performance with that of Auto-ML. The results prove that Auto-ML is capable of building models of similar accuracies in a time span of 30 minutes per data-set, with just a few lines of code. This should provide enough empirical evidence and encourage doctors to adopt ML as part of their research. Proposal 2 discusses the power of visualization of Convolutional Neural Networks (CNN) in performing classification tasks and how they help develop trust in doctors and medical researchers about model predictions. Gradient-weighted Class Activation Mapping (Grad-CAM) is used as a tool to generate localization maps indicating regions in the image that contributed to a certain prediction from the CNN, thereby instilling trust in medical professionals.Electrical and Computer Engineering, Department o

Multi-stage resource-aware scheduling for data centers with heterogeneous servers

This paper presents a three-stage algorithm for resource-aware scheduling of computational jobs in a large-scale heterogeneous data center. The algorithm aims to allocate job classes to machine configurations to attain an efficient mapping between job resource request profiles and machine resource capacity profiles. The first stage uses a queueing model that treats the system in an aggregated manner with pooled machines and jobs represented as a fluid flow. The latter two stages use combinatorial optimization techniques to solve a shorter-term, more accurate representation of the problem using the first-stage, long-term solution for heuristic guidance. In the second stage, jobs and machines are discretized. A linear programming model is used to obtain a solution to the discrete problem that maximizes the system capacity given a restriction on the job class and machine configuration pairings based on the solution of the first stage. The final stage is a scheduling policy that uses the solution from the second stage to guide the dispatching of arriving jobs to machines. We present experimental results of our algorithm on both Google workload trace data and generated data and show that it outperforms existing schedulers. These results illustrate the importance of considering heterogeneity of both job and machine configuration profiles in making effective scheduling decisions. Keywords: Resource-aware scheduling, Dynamic scheduling, Heterogeneous serversGoogle (Firm) (Research Award)Natural Sciences and Engineering Research Council of Canad

Arti fi cial intelligence and machine learning in

Arti ficial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist ?s ability to extract information on diagnosis, prognosis, and therapy responsiveness from native or transplant kidney biopsies. Although AI can be used to analyze a wide variety of biopsy -related data, this review focuses on whole slide images traditionally used in nephropathology. AI applications in nephropathology have recently become available through several advancing technologies, including (i) widespread introduction of glass slide scanners, (ii) data servers in pathology departments worldwide, and (iii) through greatly improved computer hardware to enable AI training. In this review, we explain how AI can enhance the reproducibility of nephropathology results for certain parameters in the context of precision medicine using advanced architectures, such as convolutional neural networks, that are currently the state of the art in machine learning software for this task. Because AI applications in nephropathology are still in their infancy, we show the power and potential of AI applications mostly in the example of oncopathology. Moreover, we discuss the technological obstacles as well as the current stakeholder and regulatory concerns about developing AI applications in nephropathology from the perspective of nephropathologists and the wider nephrology community. We expect the gradual introduction of these technologies into routine diagnostics and research for selective tasks, suggesting that this technology will enhance the performance of nephropathologists rather than making them redundant