En el marco de "IEEE Instrumentation & Measurement Society
Distinguished Lecture Program", el Prof. Dr. Jacob Scharcanski
realizará la charla:
Modeling Issues in Medical Imaging Measurements
Miércoles 23 de Julio, 10:00hs, Salón Azul, Facultad de Ingeniería.
Resumen:
In this talk, modeling in medical imaging and measurements is proposed as a way to
facilitate the interpretation of phenomena based on medical imagery, or to make
inferences based on models of such phenomena. In order to illustrate this presentation,
several modeling issues in medical imaging and measurements are discussed, and
illustrated by examples.
When modeling imaging measurements, usually we are trying to describe the world (or a
real world phenomenon) using one or more images, and reconstruct some of its properties
based on imagery data (like shape, texture or color). Actually, this is an ill-posed problem
that humans can learn to solve effortlessly, but computer algorithms often are prone to
errors. Nevertheless, in some cases computers can surpass humans and help interpret
imagery more accurately, given the proper choice of models, as we will discuss in this
talk.
Modeling medical imaging measurements often involves errors, and estimating the
expected error of a model can be important in some applications (e.g. when estimating a
tumor size and its potential growth, or shrinkage, in response to treatment). Typically, a
model has tuning parameters, and these tuning parameters may change the model
complexity. We wish to minimize modeling errors and the model complexity, in other
words, to get the ‘big picture’ we often sacrifice some of the small details. For example,
estimating tumor growth (or shrinkage) in response to treatment requires modeling the
tumor shape and size, which can be challenging for real tumors, and simplified models
may be justifiable if the predictions obtained are informative (e.g. to evaluate the
treatment effectiveness). This issue is closely related to machine learning and pattern
recognition, and techniques of these areas can be adapted to resolve problems in medical
imaging measurements. To conclude this talk, open problems in medical imaging
measurements and model selection are discussed in some detail.
facilitate the interpretation of phenomena based on medical imagery, or to make
inferences based on models of such phenomena. In order to illustrate this presentation,
several modeling issues in medical imaging and measurements are discussed, and
illustrated by examples.
When modeling imaging measurements, usually we are trying to describe the world (or a
real world phenomenon) using one or more images, and reconstruct some of its properties
based on imagery data (like shape, texture or color). Actually, this is an ill-posed problem
that humans can learn to solve effortlessly, but computer algorithms often are prone to
errors. Nevertheless, in some cases computers can surpass humans and help interpret
imagery more accurately, given the proper choice of models, as we will discuss in this
talk.
Modeling medical imaging measurements often involves errors, and estimating the
expected error of a model can be important in some applications (e.g. when estimating a
tumor size and its potential growth, or shrinkage, in response to treatment). Typically, a
model has tuning parameters, and these tuning parameters may change the model
complexity. We wish to minimize modeling errors and the model complexity, in other
words, to get the ‘big picture’ we often sacrifice some of the small details. For example,
estimating tumor growth (or shrinkage) in response to treatment requires modeling the
tumor shape and size, which can be challenging for real tumors, and simplified models
may be justifiable if the predictions obtained are informative (e.g. to evaluate the
treatment effectiveness). This issue is closely related to machine learning and pattern
recognition, and techniques of these areas can be adapted to resolve problems in medical
imaging measurements. To conclude this talk, open problems in medical imaging
measurements and model selection are discussed in some detail.
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Prof. Jacob Scharcanski is an Associate Professor in Computer Science at the Federal
University of Rio Grande do Sul (UFRGS), Brasil. He holds a cross appointment
with the Department of Electrical Engineering at UFRGS, and also is an Associate
Adjunct Professor with the Department of Systems Design Engineering, University
of Waterloo, Ontario, Canada. He has authored and co-authored over 130 refereed
journal and conference papers, and has contributed to several books on imaging
and measurements. In addition to his academic publications, he has several
technology transfers to the private sector. Presently, he serves as an Associate
Editor for two journals, and has served on dozens of International Conference
Committees. Professor Scharcanski is a licensed Professional Engineer, Senior
Member of the IEEE, and serves as Chair of the Technical Committee IEEE IMS TC-
17 (Materials in Measurements) and as Co-Chair of the Technical Committee IEEE
IMS TC-19 (Imaging Measurements and Systems). His areas of expertise are
Instrumentation, Imaging Measurements and their Applications.