Latest News Archive

Please select Category, Year, and then Month to display items
Previous Archive
02 May 2019 | Story Xolisa Mnukwa
UFS Debate Society
The UFS Debate Society led by example at the 2019 Jozi Rumble.

After competing in the Jozi Rumble final for six consecutive years, the UFS Debate Society won the competition – Africa’s largest intercollegiate debate open – for the second consecutive year. The tournament took place at the University of the Witwatersrand (Wits) in April 2019.

After seven preliminary rounds, three UFS teams out of a total of 100 competing teams overall were placed in the top 16, earning them a place in the quarterfinals – where they faced each other. A composite team of UFS LLB graduate and LLM student, Lehakoe Masedi, and a partner from Wits beat all teams, qualified for the final themed ‘This house regrets the glorification of opulence in popular culture’, and won the league.

“It was one of the most validating moments of my entire debating career; everybody wants to win the Jozi Rumble, and to have done it and to be the best speaker is truly amazing,” said Lehakoe. The top-ranking speaker at the tournament added that she had been working hard, and that she is glad that her efforts are coming full circle.

The UFS sent six teams overall to the tournament, including two novice teams competing in their first-ever intercollegiate debate tournament. 

“Speaking at the Jozi Rumble debate tournament for the first time was truly an educational experience; it exposed me to the dynamics of varsity-level debating,” said Simphiwe Yana, debater in of the UFS novice teams.

The UFS speaking squad consisted of Lehakoe Masedi, 2018 Abe Bailey Bursary victor and Rhodes scholarship recipient Nkahiseng Ralepeli, Khotso Khokho, Siyanda Rixana, Morena Moabi, Simphiwe Yana, Luvuyo Shoco, Asemahle Noholoza, and Nontobeko Msimangu. Former Chairperson of the UFS Debate Society and Editor-in-Chief of the IRAWA newspaper, Tshiamo Malatji, was also present at the tournament as the Tabulation Director. 

On 11 May 2019, the UFS will travel to the University of Pretoria to defend yet another debate open title at the Pretoria Parlay Intervarsity. 


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.

We use cookies to make interactions with our websites and services easy and meaningful. To better understand how they are used, read more about the UFS cookie policy. By continuing to use this site you are giving us your consent to do this.

Accept