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19 April 2021 | Story NONSINDISO QWABE | Photo Supplied
LLB graduate Tshepang Mahlatsi

 
‘Be loyal to your calling and the universe will locate you.’ This slogan is the mantra that University of the Free State LLM student, Tshepang Mahlatsi, lives by. It is also this slogan that carried him through a tumultuous journey during the pursuit of his LLB degree, which he received during the Bloemfontein Campus graduation ceremony on 19 April. 

Mahlatsi began his LLB degree in 2014, but he had to take a break from his academics in 2016 after being clinically diagnosed with depression. He obtained his qualification in 2020. Mahlatsi said 2016 was a year that started on a high note for him as a third-year Law student and newly elected prime for Tswelopele residence, but quickly took a downward dive when he found himself overwhelmed by leadership demands – coupled with the simultaneous loss of loved ones and constant academic pressure. It ultimately led to a breakdown, forcing him to put his studies on hold. "I am graduating with my LLB after life-changing events in my undergraduate years – from student politics, depression, and PTSD, to starting a mental-health organisation and using both CUADS and Kovsie Counselling support services to come back to ‘normalcy’.”

He said the year-long break from his studies left him feeling discouraged as he watched his peers and classmates progress and graduate. "It was the most difficult thing to do to remind myself that I wasn't stupid." 

"This journey exposed a lot about myself; it exposed that with determination and resilience, you can achieve what you set out to achieve. I had to persevere not because I wanted to, but because my family has never seen a graduate. I was doing this for them; to give them something they've never had,” he said. 

UFS support services can save lives 

Mahlatsi would like more students to make use of the UFS support services and not crumble under mental-health problems. "I hope to inspire students to use their support services and not be ashamed – services such as CUADS and Student Counselling and Development. I hope to inspire student leaders and students to realise that you can be a well-rounded student and still have challenges, but eventually, success awaits us all."

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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