Machine Learning in Medicine Seminar

  • Remote Access - Zoom
  • Virtual Presentation
  • Institute Professor and Department Chair
  • Department of Computer Science and Engineering
  • NYU Tandon School of Engineering, New York University

Time will tell: Longitudinal Image Analysis to meet Clinical Needs

Clinical assessment routinely uses terms such as development, growth trajectory, aging, degeneration, disease progress, recovery or prediction. This terminology inherently carries the aspect of dynamic processes, suggesting that measurement of dynamic spatiotemporal changes may provide information not available from single snapshots in time. Image processing of temporal series of 3-D data embedding time-varying anatomical objects and functional measures requires a new class of analysis methods and tools that makes use of the inherent correlation and causality of repeated acquisitions. This talk will discuss progress in the development of advanced 4-D image and shape analysis methodologies that carry the notion of linear and nonlinear regression, now applied to complex, high-dimensional data such as images, image-derived shapes and structures, or a combination thereof. We will demonstrate that statistical concepts of longitudinal data analysis such as linear and nonlinear mixed-effect modeling, commonly applied to univariate or low-dimensional data, can be extended to structures and shapes modeled from longitudinal image data, ranging from modeling of changes of shape, image contrast up to ODFs in diffusion MRI. Most relevant to clinical studies, we will also cover inclusion of subject’s covariates such as sex and diagnostic scores, into longitudinal image and shape analysis.

The rapidly increasing role of learning-based techniques, enabled by the availability of large publicly shared image databases, will be discussed related to crucial aspects of longitudinal imaging such as image harmonization, automated quality control and correction, and image curation and synthesis. We will discuss results from ongoing clinical studies such as analysis of early brain growth in controls and subjects at risk for autism, analysis of neurodgeneration in normal aging and Huntington's disease, and quantitative assessment of progression of glaucoma.

Guido Gerig is Department Chair and Institute Professor at the NYU Tandon School of Engineering in the Department of Computer Science and Engineering. He also holds associated/affiliated appointments at NYU Courant CS, NYU Langone Child and Adolescent Psychiatry, Psychiatry and Radiology. Guido Gerig has been named IEEE Fellow (class of 2019) and has also been appointed as a Fellow of the American Institute for Medical  and  Biological  Engineering (AIMBE) in 2010. He was previously USTAR Professor of Computer Science at the University of Utah (2007-2015) establishing the Utah Center for Neuroimage Analysis (UCNIA), Taylor Grandy Professor of Computer Science and Psychiatry at the University of North Carolina at Chapel Hill (1998-2007) launching the UNC Neuro Image Research and Analysis Laboratories (NIRAL), and Assistant Professor at ETH Zurich (1993-1998). Gerig holds several awards from Utah and UNC for Excellence in Teaching.

About the MLxMed Seminar Series: Medicine is complex and data-driven while discovery and decision making are increasingly enabled by machine learning. Machine learning has the potential to support, enable and improve medical discovery and clinical decision making in areas such as medical imaging, cancer diagnostics, precision medicine, clinical trials, and electronic health records. This series focuses on new algorithms, real-world deployment, and future trends in machine learning in medicine. It will features prominent investigators who are developing and applying machine learning to biomedical discovery and in clinical decision support.

In cooperation with the Department of Biomedical Informatics / PITT / CMU / UMPC

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