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13 August 2024
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Story Anthony Mthembu
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Photo Sibahle Dayimani and Amandla Kulu
Prof Peter Roseel, Managing Director of Management Consulting and Research – a spin-off of the Catholic University of Leuven in Belgium; and Prof Nicolene Barkhuizen, Director of the UFS Business School.
The Business School at the University of the Free State (UFS) hosted Prof Peter Rosseel, Managing Director of Management Consulting and Research – a spin-off of the Catholic University of Leuven in Belgium – for a guest lecture during his visit to the UFS Faculty of Economic and Management Sciences (EMS).
The guest lecture took place on 19 July 2024 in the Business School Auditorium and was attended by academics from the UFS.
Reflecting on the lecture
The lecture presented by Prof Rosseel focused on how combining strategy, strategy implementation, culture transformation, leadership, and learning successfully leads to sustainable growth, creates engagement, and delivers tangible results. Throughout the lecture, Prof Rosseel spoke about how experts tend to make bad leaders and therefore stop change from happening within an organisation. In fact, he highlighted that, “Experts stop change from happening within the workplace because experts, by definition, look through the eyes of their expertise, but you cannot reduce the world to different forms of expertise, as it is holistic.” As such, he argued that to change an organisation, one must see things from the point of view of others.
Furthermore, Prof Rosseel delved deeper into the hierarchical operating model within organisations. He indicated that the above model should be one community within organisations; however, unfortunately it is not. This is because organisations are made up of several departments such as finance and human resources. As such, he regards these departments to be silos that could prove to be detrimental to organisations, as each silo can create its own culture as opposed to an organisational culture. These are some of the points he discussed throughout the lecture.
After the lecture concluded, the audience had the opportunity to engage with Prof Rosseel on his viewpoints. In fact, Lyle Markham, Academic Head of Department and Lecturer in Industrial Psychology at the UFS, was one of the audience members and described the lecture as insightful.
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.