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EMS Faculty hosts Inaugural Debate in Broadening Curricular Debate series

29 November 2023 | Story Anthony Mthembu | Photo Anthony Mthembu and Reabetswe Parkies

The Economics and Management Sciences (EMS) Faculty at the University of the Free State (UFS) recently inaugurated its first Broadening Curricular Debate series, a concept conceived by the Dean of the Faculty, Prof Phillipe Burger. The inaugural debate, held on 22 November 2023 in the Equitas Senate Hall on the UFS Bloemfontein Campus, marked the beginning of a series designed to facilitate discussions among academics on crucial higher education matters. Annari Muller, Chairperson of the Learning, Teaching and Digitisation Committee (LTDC), expressed the series’ purpose: “We organised this debate series to provide a platform for academics to discuss vital higher education matters. These sessions aim to stimulate critical conversations that empower UFS staff to enrich our curricula, enhance teaching practices, and shape broader educational strategies.’’

The motion presented to the house was, ‘The rapid integration of Artificial Intelligence in higher education perpetuates educational inequalities, widens the digital divide, and diminishes the value of personal instruction. The debate followed the structure of Intelligence Squared debates, with two teams comprising UFS staff from diverse departments, including the Department of Business Management, Department of English, Department of Public Management and the Department of Mathematical Statistics and Actuarial Science.

Naquita Fernandes, the Master of the House for the debate, emphasised the deliberate inclusion of members from diverse fields to infuse varied perspectives into the debate. “We believed that this diverse amalgamation of expertise would offer multifaceted insights, ensuring a holistic exploration of the subject matter. The debate structure was meticulously designed to encourage engaging discussions rather than formal academic presentations, allowing for a robust exchange of ideas.’’

The audience had the opportunity to vote on their stance before and after the debate, determining the winning team based on their ability to sway the audience with compelling arguments. The winning team, composed of Dr Hilary Bama (Senior Lecturer in the Department of Business Management), Dr Martin Rossouw (Senior Lecturer in Film and Visual Media), and Dr Rick De Villiers (Senior Lecturer in the English Department), successfully argued against the motion.

The proposition team highlighted the existing gap between those with access to digital technologies and those without, advocating for a gradual and considered approach to AI integration in higher education. In contrast, the opposition team underscored the value of personal instruction in the face of AI, emphasising AI’s potential to provide constructive and effective feedback, contribute to adaptive learning platforms, and accommodate unique learning styles and preferences.

Following the debate, the audience was addressed by a team from Carnegie Math Pathways, providing insights into generative AI tools. Fernandes described the event as a proactive step in shaping the UFS academic landscape, moving away from reactive responses and exploring critical topics and strategies that could influence future policies and practices.

EMS Faculty hosts Inaugural Debate in Broadening Curricular Debate series

The Debaters- From left to right: Dr Martin Rossouw; Senior Lecturer in Film and Visual Media, Herkulaas Michael Combrink; Co-Director of Digital Futures, Dr Hilary Bama; Senior Lecturer in the EMS Faculty, Dr Rick De Villiers; Lecturer in the Department of English, Dr Michele Von Maltitz; Senior Lecturer in the Department of Mathematical Statistics and Actuarial Science, and Nkosingiphile Emmanuel Mkhize; Lecturer and Researcher in the Department of Public Management.

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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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