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07 March 2024
Photo Lunga Luthuli
Prof Francois Strydom, Senior Director at the Centre for Teaching and Learning and Simphiwe Kunene, the first African DREAM scholar and a master’s student from the Faculty of Education.
The University of the Free State (UFS) received recognition for its commitment to student success at the 2024 Achieving the Dream (ATD) conference which celebrated its 20-year anniversary. Simphiwe Kunene, an Education master's student originally from the Qwaqwa campus, was selected to represent South Africa as the first DREAM scholar from Africa as part of the conference.
The Achieving the Dream network of over 300 institutions, which is one of the largest movements in US higher education aims to transform colleges and universities so that students of colour and a lower socio-economic status are supported to earn a degree. The UFS is a leading partner in the Siyaphumelela Network, which has been working for 10 years with ATD to enhance the success of students in South Africa. Prof Francois Strydom, Senior Director at the Centre of Teaching and Learning (CTL), accepted the award on behalf of the institution.
Prof Strydom said that collaboration with the ATD and Siyaphumelela institutions has helped the UFS to develop cutting-edge approaches to “level the playing field” and support Kovsies to earn their undergraduate degrees.
The first African DREAM scholar
Kunene was selected as the first African DREAM scholar from the Siyaphumelela network. To select the DREAM scholar, each Siyaphumelela partner institution nominated one student as a preferred candidate. From the proposed candidates, the DREAM scholar was selected by the South African Institute for Distance Education (Saide) based on the following: demonstrating resilience, academic excellence, and a deep commitment to making a positive difference in universities and personal communities.
He addressed the conference of over 2 000 delegates and shared with them his hopes and dreams. Many members of the South African delegation said Simphiwe did his country proud. He had the following to say about his opportunity to be a DREAM scholar: “Being a DREAM scholar was life changing for me, exposing me to an array of opportunities I never knew were possible and available for me. It was as if, for a moment, the world had stopped to just listen to what I had to say."
The way forward
The UFS will continue its work as a partner of the Siyaphumelela network for the 2024-2026 cycle. The multi-stakeholder project team is focused on enhancing undergraduate students’ time, and to position the UFS as a thought and research leader in the area of student success as part of Vision 130.
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.