Realiza-se no dia 12 de abril, pelas 14h00, no auditório 2 do Edifício B da ESTG, a palestra do Prof. Jacob Scharcanski da Universidade Federal do  Rio Grande do Sul, Brasil, subordinada ao tema “Computer Vision in Medical Imaging and Measurements: Making Sense of Visual Data”. A palestra será realizada em português.

Title: Computer Vision in Medical Imaging and Measurements: Making Sense of Visual Data

In this talk, computer vision 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. Often, 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.

 Jacob Scharcanski is a Professor (Full) in Computer Science at the Federal University of Rio Grande do Sul (UFRGS), Brazil. He holds a cross appointment with the Department of Electrical Engineering at UFRGS, and also is an Adjunct Professor with the Department of Systems Design Engineering, University of Waterloo, Canada. He authored and co- authored over 150 refereed journal and conference papers, book chapters and books, and delivered over 30 invited presentations worldwide. He serves as an Associate Editor for two journals, and has served on dozens of International Conference Committees. In addition to his academic activities, he has several technology transfers to the private sector. Professor Scharcanski is a licensed Professional Engineer (PEO, Canada), Senior Member of the IEEE, Member of SPIE, and serves as Co-Chair of the Technical Committee IEEE IMS TC-19 (Imaging Measurements and Systems).