Biomedical Imaging is quintessential in disease understanding. Its ubiquity is evident across imaging scales ranging from cellular and microscopy imaging for molecular/biological mechanisms, pathology imaging for tissue-level analysis, radiological imaging for within body diseases diagnosis to optical imaging for more phenotypical symptoms. Biomedical imaging is also at an inflection point, where advances in artificial intelligence could be used to integrate our understanding of disease states through individual lenses of clinical, biological, radiological, and pathological perspectives to form a more holistic understanding of the disease for improved diagnostics.

To explore issues of imaging state-of-the-art, use of artificial intelligence in healthcare, and diagnostic needs by practitioners, a symposium is being organized on February 4-5, 2020, at IBM Almaden Research Lab located at 650 Harry Road, San Jose, CA 95120. ​​​​

The symposium aims to bring together researchers, practitioners, and industrial partners to explore the role of artificial intelligence in understanding the mechanisms of diseases such as cancer, cardiovascular and metabolic diseases across imaging scales. In doing so, the long-term goal is to advance our understanding of disease by integrating the learning across the scale and time from molecular, cellular, tissue level (pathological), radiological, and optical image analysis. The symposium will feature talks from renowned scientists, clinicians and industrial partners covering the latest advancements in the use of AI for imaging across scales for disease understanding. The symposium will also feature other talks and demos by researchers in academia & industry including a startup showcase. ​​​​

Keynote Speakers

Nobel Laureate W.E. Moerner, PhD

William Esco Moerner is an American physical chemist and chemical physicist with current work in the biophysics and AI for imaging of single molecules. He is credited with achieving the first optical detection and spectroscopy of a single molecule in condensed phases, along with his postdoc, Lothar Kador. Prof. Moerner won the Nobel Prize in Chemistry in 2014 for the development of super-resolved fluorescence microscopy.

Geraldine B. McGinty, MD, MBA, FACR

Geraldine McGinty M.D., MBA, FACR, is an American physician. She is currently Chair of the American College of Radiology Board of Chancellors. She is the first woman to hold this post. Previously, she served as Chair of the American College of Radiology Commission on Economics.

Anant Madabhushi , PhD

Anant Madabhushi is the F. Alex Nason professor of biomedical engineering at Case Western Reserve University in Cleveland and director of the university’s Center for Computational Imaging and Personalized Diagnostics (CCIPD). Dr. Madabhushi'steam has developed pioneering computer aided diagnosis, pattern recognition, image analysis tools for diagnosis and prognosis of different types of cancers based on quantitative and computerized histomorphometric image analysis of digitized histologic biopsy tissue specimens as well as from radiographic scans such as MRI and CT images.

Abstract: "Quantitative Data Convergence: Integration of radiology, pathology, omics data for predicting disease aggressiveness and patient outcome"
Abstract: Traditional biology generally looks at only a few aspects of an organism at a time and attempts to molecularly dissect diseases and study them part by part with the hope that the sum of knowledge of parts would help explain the operation of the whole. Rarely has this been a successful strategy to understand the causes and cures for complex diseases. The motivation for a systems based approach to disease understanding aims to understand how large numbers of interrelated health variables, gene expression profiling, its cellular architecture and microenvironment, as seen in its histological image features, its 3 dimensional tissue architecture and vascularization, as seen in dynamic contrast enhanced (DCE) MRI, and its metabolic features, as seen by Magnetic Resonance Spectroscopy (MRS) or Positron Emission Tomography (PET), result in emergence of definable phenotypes. At the Center for Computational Imaging and Personalized Diagnostics at Case Western Reserve University, we have been developing computerized knowledge alignment, representation, and fusion tools for integrating and correlating heterogeneous biological data spanning different spatial and temporal scales, modalities, and functionalities. The long term research objectives are to explore via efficient computational and pattern recognition methods, the existence and correspondence of biological patterns across heterogeneous data scales and modalities. An understanding of the interplays of different hierarchies of biological information from proteins, tissue, metabolites, and imaging will provide conceptual insights and practical innovations that will profoundly transform people's lives.

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