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18 February 2021 | Story ANDRE DAMONS | Photo Supplied
Prof Maxim Finkelstein, distinguished Professor at the Department of Mathematical Statistics and Actuarial Science at the UFS has become the only researcher with an A1-rating in South Africa (awarded by NRF) in Probability, Statistics and Operations Research.

A professor in the Faculty of Natural and Agricultural Sciences at the University of the Free State (UFS) has become the only researcher with an A1-rating in Probability, Statistics and Operations Research in South Africa after being awarded this prestigious rating by the National Research Foundation (NRF).

This is the second time Prof Maxim Finkelstein, the distinguished Professor at the Department of Mathematical Statistics and Actuarial Science in the Faculty of Natural and Agricultural Sciences, has been awarded with an A-rating. The first was in 2015.

The goal is to produce quality research

According to Prof Finkelstein, the rating should not be a goal as such for a researcher but should produce a quality research that is recognised by peers and that, above all, brings a real satisfaction in life. Prof Finkelstein says: “The rating is just a consequence of what one, as a researcher, has achieved in the past eight years and, actually, during the whole professional life as well. South Africa is the only country in the world that is able to perform this rigorous internationally sound rating process for individual researchers. ‘Scientifically large’ countries just cannot do it, technically.”

Prof Finkelstein’s area of expertise is the modelling of random events and quantifying probabilities of their occurrences. He explains: “For instance, in industry, people are interested in probabilities that a machine or process or mission will accomplish its task without failure or accident. In order to assess the probabilities of interest, one must have an adequate mathematical/stochastic model that should be properly developed. 

“Thus, I am developing such models that can be rather advanced because they should take into account numerous factors, e.g., that the object is operating in a random environment, that its structure could change, that there can be human errors affecting the outcome, that an object interacts with other objects, etc. This is usually done in the framework of mathematical reliability theory that considers operation of technical devices.” 

The only A-rating at NAS

“I am quite excited to get the A-rating for the second time, especially because it is the only A-rating in Probability, Statistics and Operations Research in South Africa. It is also the only A-rating at our Faculty of Natural and Agricultural Sciences.

“The fact that it is an A1 and not A2, as previously, does not, in fact, mean too much to me. What matters really is that it is the A-category defined by the reviewers’ opinions that the applicant is a world leader in his discipline,” says Prof Finkelstein.

During his numerous visits as a research professor to the Max Planck Institute of Demographic Research in Germany, he jointly with the colleagues from this institute, were applying the developed stochastic approaches to modelling lifespans of organisms as well. 

One of Prof Finkelstein’s evolving interests is in the area of healthcare engineering when, for instance, monitoring the key health parameters of a patient, some optimal cost-wise decisions can be made on preventive treatments and interventions. 

“I want also to stress that, in general, international collaboration is very important for emerging and established researchers, especially in ‘remote’ South Africa, although nowadays the term ‘remote’ is obviously outdated,” says Prof Finkelstein.

He also collaborates with numerous colleagues around the globe. Apart from the visiting position in the Max Planck Institute he held for many years, Prof Finkelstein regularly visits the ITMO University in St Petersburg, Russia, and is also now establishing a Visiting Professor position at the University of Strathclyde in Glasgow, Scotland.

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Mathematical methods used to detect and classify breast cancer masses
2016-08-10

Description: Breast lesions Tags: Breast lesions

Examples of Acho’s breast mass
segmentation identification

Breast cancer is the leading cause of female mortality in developing countries. According to the World Health Organization (WHO), the low survival rates in developing countries are mainly due to the lack of early detection and adequate diagnosis programs.

Seeing the picture more clearly

Susan Acho from the University of the Free State’s Department of Medical Physics, breast cancer research focuses on using mathematical methods to delineate and classify breast masses. Advancements in medical research have led to remarkable progress in breast cancer detection, however, according to Acho, the methods of diagnosis currently available commercially, lack a detailed finesse in accurately identifying the boundaries of breast mass lesions.

Inspiration drawn from pioneer

Drawing inspiration from the Mammography Computer Aided Diagnosis Development and Implementation (CAADI) project, which was the brainchild Prof William Rae, Head of the department of Medical Physics, Acho’s MMedSc thesis titled ‘Segmentation and Quantitative Characterisation of Breast Masses Imaged using Digital Mammography’ investigates classical segmentation algorithms, texture features and classification of breast masses in mammography. It is a rare research topic in South Africa.

 Characterisation of breast masses, involves delineating and analysing the breast mass region on a mammogram in order to determine its shape, margin and texture composition. Computer-aided diagnosis (CAD) program detects the outline of the mass lesion, and uses this information together with its texture features to determine the clinical traits of the mass. CAD programs mark suspicious areas for second look or areas on a mammogram that the radiologist might have overlooked. It can act as an independent double reader of a mammogram in institutions where there is a shortage of trained mammogram readers. 

Light at the end of the tunnel

Breast cancer is one of the most common malignancies among females in South Africa. “The challenge is being able to apply these mathematical methods in the medical field to help find solutions to specific medical problems, and that’s what I hope my research will do,” she says.

By using mathematics, physics and digital imaging to understand breast masses on mammograms, her research bridges the gap between these fields to provide algorithms which are applicable in medical image interpretation.

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