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29 August 2024
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Story Anthony Mthembu
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Photo Harmse Photography
Ross van Reenen, CEO of the Toyota Free State Cheetahs.
The Business School at the University of the Free State (UFS) recently hosted the CEO of the Toyota Free State Cheetahs, Ross van Reenen, for a guest lecture. Van Reenen presented the guest lecture in the Business School Auditorium on the UFS Bloemfontein Campus on 21 August 2024.
Reflecting on Van Reenen’s address
In a lecture addressed to an auditorium filled with UFS staff and students, Van Reenen’s talk was divided into several sections. Firstly, he spoke about the concept of the ‘black swan’ in reference to the book written by Nassim Nicholas Taleb, titled The Black Swan: The Impact of the Highly Improbable. Referring to Taleb’s book, Van Reenen defined a ‘black swan’ as a rare event that has a severe impact, and the occurrence of which people try to explain. He used some examples to explore this concept as well as its implications, including COVID-19 and its impact on the world, and the tragic death of the people in the Titanic disaster. However, he delved deeper into 9/11 and the extent of its impact, saying that “9/11 was a major wake-up call in the world economy”. Van Reenen highlighted how some companies such as Barclays, for which he previously consulted, had to work to be up and running after the collapse of the Twin Towers in 9/11.
In addition, Van Reenen’s lecture also touched on the importance of the first ninety days of a job after an individual has been employed. “Those first ninety days are crucial, as you have to establish yourself in a company where you are paid less than you are worth,” Van Reenen said. As such, he gave the audience insight into what they could focus on in that time frame. This includes focusing on the small wins, as well as ensuring that you are working at keeping the team together, as the team is an integral part of an organisation.
Van Reenen concluded his address by speaking about his time as the CEO of the Toyota Free State Cheetahs, including some of the decisions he took to ensure the success of the organisation.
Mathematical methods used to detect and classify breast cancer masses
2016-08-10
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.