Exascale Deep Learning to Accelerate Cancer Research

Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural network architecture (e.g. number of layers, types of layers, connections between layers, etc.) plays a critical role in determining what, if anything, the neural network is able to learn from the training data. The trend for neural network architectures, especially those trained on ImageNet, has been to grow ever deeper and more complex. The result has been ever increasing accuracy on benchmark datasets with the cost of increased computational demands. In this paper we demonstrate that neural network architectures can be automatically generated, tailored for a specific application, with dual objectives: accuracy of prediction and speed of prediction. Using MENNDL--an HPC-enabled software stack for neural architecture search--we generate a neural network with comparable accuracy to state-of-the-art networks on a cancer pathology dataset that is also $16\times$ faster at inference. The speedup in inference is necessary because of the volume and velocity of cancer pathology data; specifically, the previous state-of-the-art networks are too slow for individual researchers without access to HPC systems to keep pace with the rate of data generation. Our new model enables researchers with modest computational resources to analyze newly generated data faster than it is collected.Comment: Submitted to IEEE Big Dat

Plasma Dynamics

Contains research objectives and summary of research on nineteen research projects split into five sections.National Science Foundation (Grant ENG75-06242-A01)U.S. Energy Research and Development Administration (Contract E(11-1)-2766)U.S. Air Force - Office of Scientific Research (Grant AFOSR-77-3143)U.S. Energy Research and Development Administration (Contract EY-76-C2-02-3070.*000

Analysis and Classification of Mammography Reports Using Maximum Variation Sampling

Currently, no automated means of detecting abnormal mammograms exist. While knowledge discovery capabilities through data mining and data analytics tools are widespread in many industries, the healthcare industry as a whole lags far behind. Providers are only just beginning to recognize the value of data mining as a tool to analyze patient care and clinical outcomes. The research conducted by the authors investigates the use of genetic algorithms for classification of unstructured mammography reports, which can be later correlated to the images for extraction and testing. In mammography, much effort has been expended to characterize findings in the radiology reports. Various computer-assisted technologies have been developed to assist radiologists in detecting cancer; however, the algorithms still lack high degrees of sensitivity and specificity, and must undergo machine learning against a training set with known pathologies in order to further refine the algorithms with higher validity of truth. In a large database of reports and corresponding images, automated tools are needed just to determine which data to include in the training set. Validation of these data is another issue. Radiologists disagree with each other over the characteristics and features of what constitutes a normal mammogram and the terminology to use in the associated radiology report. Abnormal reports follow the lexicon established by the American College of radiology Breast Imaging Reporting and Data System (Bi-RADS), but even within these reports, there is a high degree of text variability and interpretation of semantics. The focus has been on classifying abnormal or suspicious reports, but even this process needs further layers of clustering and gradation, so that individual lesions can be more effectively classified. The tools that are needed will not only help further identify problem areas but also support risk assessment and other knowledge discovery applications. The knowledge to be gained by extracting and integrating meaningful information from radiology reports will have a far-reaching benefit, in terms of the refinement of the classifications of various findings within the reports. This will support validation, training and optimization of these and other machine learning and computer-aided diagnosis algorithms to work both in this environment and with other medical and imaging modalities. In the near-term, the objective of this work is to accurately identify abnormal radiology reports amid a massive collection of reports. The challenge in achieving this objective lies in the use of natural language to describe the patient's condition. The premise of this work is that abnormal radiology reports consist of words and phrases that are statistically rare or unusual. If this is true, then it is expected that abnormal reports will be significantly dissimilar in comparison to normal radiology reports. To achieve this objective, our approach employs maximum variation sampling (MVS), which is implemented as an adaptive sampling approach. Maximum variation sampling seeks to identify a particular sample of data that will represent the diverse data points in a data set. Adaptive sampling continues to draw samples from the population based on previous samples until some criteria have been met. Previous results from using MVS indicated that an ideal sample could be found very quickly using this approach