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17 August 2022 | Story Edzani Nephalela and Coreen Steenkamp | Photo Francois van Vuuren
Academic Leadership Programme
The new cohort of the Academic Leadership Programme.

Educational leaders serve a significant administrative, management, and leadership function in higher education. A departmental chair’s role differs fundamentally from other leadership contexts, based on the momentous transition from being an academic by profession to providing leadership at departmental level.
The Academic Leadership Programme (APL) was launched by the University of the Free State (UFS) Centre for Teaching and Learning (CTL) to equip academics for various managerial positions. Faculty deans propose candidates for this programme; the second cohort has been chosen as the first is nearing completion. 
The first workshop commenced with an engagement with the Rector and Vice-Chancellor of the UFS, Prof Francis Petersen, and the Vice-Rector: Academic, Dr Engela van Staden, who both shared strategic academic leadership perspectives during the orientation and welcoming of the APL. 
Such reflections highlighted the expectations of being an educator, the complexity, and the critical role of departmental chairs within higher education institutions. Academic leaders are thus expected to establish firm leadership within their departments, facilitate intellectual development, manage administrative duties, and strive toward resilient learning and teaching environments. 
“The position of departmental chairs remains critical for any higher education institution, as they provide leadership in advancing the discipline, teaching students, producing quality graduates, and serving the professional community,” said Prof Francois Strydom, Senior Director: Centre for Teaching and Learning.
Research confirms that most academics succeed in these roles without formal leadership training, yet the expectation of developing or having certain leadership qualities or management competencies must fulfil the various functions of such a position. 


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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