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08 October 2019 | Story Xolisa Mnukwa | Photo Charl Devenish
Gradstar UFS
The 2019 GradStar programme is all about producing well-rounded students and providing them with opportunities in the world of work, explained Head of UFS Career Services, Belinda Janeke.
Congratulations to the Kovsies top-11 students who made it into the GradStar top-100 programme for 2019!

Each year, 100 South African students are selected through a rigorous four-phase judging process to become part of the GradStar programme. The programme is designed to offer opportunities for employment to previously unrecognised students.

What makes the top 11? 

According to the UFS Head of Career Services, Belinda Janeke, the GradStar programme is all about producing a well-rounded student. Approximately 6 000 applications were received from Kovsies, of which 500 were selected based on a personality test. Another test was given to the 500 students who passed the personality test, after which interviews were conducted to determine the top 100 from the UFS.  

The students who were selected to represent the UFS exhibited the most potential as future leaders in their respective fields. Apart from academic achievement, contestants were evaluated according to their individual soft skills such as motivation, discipline, altruism, and attitude. This combination promised to deliver top candidates for future employers. 

2019 GradStar programme experiences

Throughout the competition, Kovsie contestants were exposed to new people and opportunities to network with various companies in their preferred career fields, where they had the opportunity to share their CVs with potential employers. Contestants were also afforded the opportunity to develop critical problem-solving skills in the world of work. The GradStar top-100 students also have a WhatsApp group where jobs are advertised.

The programme was valuable for the Kovsies; not only did it prepare them for employment, but also provided them with an opportunity for learning and recognising their own strengths and weaknesses as individuals in the working world. 

Congratulations to the Kovsies who made it into the GradStar top 100: 

Mariné du Toit: Bachelor of Social Work
Nyiko Maluleka: Bachelor of Arts, Corporate and Marketing Communication
Bianca Malan: Bachelor of Accounting, Financial Accounting
Boitumelo Mancoe: Master of Business Administration
Kabelo Mashego: Bachelor of Medicine and Bachelor of Surgery (MB ChB)
Kananelo Moletsane: Bachelor of Agriculture
Mudzunga Mukwevho: Bachelor of Accounting, Financial Accounting
Neo Roberts: Bachelor of Science, Information Technology 
Refilwe Maimane: Bachelor of Commerce, Accounting 
Themba Makhoba: Bachelor of Public Administration
Mpolokeng Mmutle: Bachelor of Commerce, Accounting

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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