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02 July 2019 | Story Leonie Bolleurs
Edwin Skhosana
Edwin Skhosana is working hard to become a successful and competent actuary one day. With him is his lecturer, Dr Michael von Maltitz.

Edwin Skhosana, an Actuarial Sciences student, was described by his lecturer, Dr Michael von Maltitz of the Department of Mathematical Statistics and Actuarial Science, as ‘very quiet’ in his Causal Inference class. 

This may sound like a compliment, but it’s not.

For Dr Von Maltitz, being quiet is definitely not encouraged – not with the new teaching methods applied in class.

“See, my class is all about engagement – getting the students to watch videos on the topics, read about the methods in question, and then come to class to grill me about things they don’t understand. This change in teaching method is extremely disconcerting for many Mathematical students, who have up until now only been taught in the ‘memorise-regurgitate’ form they had ever since the start of high school,” he explains.

Future success


“My goal is to get the students to a level of understanding where they can sit down with me or with an expert in the field and have a conversation about the Mathematical Statistics topics that I teach. This is a very difficult task in such a technical module, and few students ever feel comfortable enough to engage with me actively in class in this way,” Dr Von Maltitz points out. 

Edwin is working hard towards applying the skills and knowledge he has obtained at university to become a successful and competent actuary one day. 

An important turning point was when it dawned on him how the things discussed in class could find an important practical application in so many fields.  

“This suddenly drove a spontaneous fascination in my mind that led me to engage with Dr Von Maltitz,” the previously quiet Edwin explains.

And everything changed.

Desperate to learn

Dr Von Maltitz explains: “Edwin came to my office to ask some questions. The incredible thing was that he sat down, and a conversation about the Mathematics, the foundations, and the methods just flowed between us. I have seldom had such an insightful chat about my module with a student. It was like a cascade of information just fell into place for Edwin.”

Although he sometimes still experiences his studies as challenging and grapples to adapt to the various styles of lecturing from different lecturers, Edwin now has hope for his class in Causal Inference. 

“I think Dr Von Maltitz’s way of presenting in class is excellent. It is, however, hard to grasp if you are still anchored in the old way of cramming, because he wants you to understand and be able to apply what he teaches,” says Edwin.

“It was just wonderfully refreshing to see someone so desperate to learn something (rather than just wanting to get a degree), and then actually managing to turn around a bad semester mark into such a river of understanding,” Dr Von Maltitz concludes.

Dr Michael von Maltitz
Dr Micheal von Maltitz

News Archive

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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