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Journal of Medical Oncology & Therapeutics | Volume 4
March 18-19, 2019 | London, UK
Oncology & Cancer Therapy
International Conference on
Knowledge-based image analysis algorithms for quantifying complexity in histology and MR/CT data
David H Nguyen
Stanford University, USA
C
urrent feature-based image analysis algorithms can identify
nuclei, cytoplasm, and stroma. These algorithms can also
detect normal tissue versus neoplastic lesions. However, feature-
based algorithms cannot detect the higher-level morphological
patterns in tumors that are reminiscent of the tissue of origin.
Furthermore, these algorithms cannot detect the degree of
recurring sub- architectures that exist in tumors of the same type
or stage (i.e. the degree of partial rosettes, the degree of subtle
cellular alignments). These recurring sub- architectures in tumors
can be precisely quantified by knowledge-based algorithms that
capturethespatialinformationinnormaltissues.Theknowledge-
based algorithms being referenced are publicly available online
(ArXiv ID’s: 1801.06752, 1710.06593, 1704.07571, 1704.07567,
1704.07567). Some of these algorithms are also applicable to
quantifying subtilties in spatial information that are present
in magnetic resonance (MR) and computed tomography (CT)
images (ArXiv ID: 1801.06752), which may be useful for refining
clinical classification of specimens.
Speaker Biography
David H Nguyen is a tumor biologist developing image analysis algorithms to advance digital
pathology for cancer diagnostics. His algorithms quantify knowledge-based features of
tissue architecture so they can be included in machine learning models that predict clinical
outcome. Dave obtained his B.A. and PhD from the University of California, Berkeley. He
is currently a Visiting Scholar in the Department of Radiology at Stanford University. Prior
to this, he was an Affiliate Scientist in the Molecular Biophysics and Integrated Bioimaging
Division at Lawrence Berkeley National Laboratory. His research interests are on Cancer
Biology, Immunohistochemistry, Cancer Cell Biology, Cancer, Tumors, Image Analysis, Tumor
Biology, Ionizing Radiation, Tumor Microenvironment, Digital Image Analysis, computational
pathology.
e
:
davidhn@stanford.edu