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04 October 2022 | Story Samkelo Fetile | Photo Supplied
Dr Sevias Guvurio
Dr Sevias Guvuriro.

Dr Sevias Guvuriro from the Faculty of Economic and Management Sciences at the University of the Free State (UFS) is the first UFS candidate to participate in the University of Michigan African Presidential Scholarship (UMAPS) fellowship programme. Dr Guvuriro is also a member of the Future Professoriate Group participating in the Transformation of the Professoriate Programme.  

About the project 

Dr Guvuriro’s main project during his five-month stay at the University of Michigan was on hazardous drinking and economic preferences among urban youth in South Africa. The project recognises that lifestyle behaviours in early life are important drivers of chronic disease later in life, and that harmful use of alcohol is among the main risk factors for non-communicable diseases in the world. According to Dr Guvuriro, persuasive behaviour-change approaches could be useful, especially in the context of developing countries, where the World Health Organisation’s non-communicable diseases ‘Best Buys’ interventions on alcohol use could be ineffective. Behavioural economics and experimental economics techniques could also be beneficial. "With the assistance of my host, Prof Erin Krupka from the University of Michigan School of Information, academics and other staff members, I have made very strong progress in analysing my survey and experimental data on the subject, which I obtained here in South Africa,” said Dr Guvuriro.

Unpacking UMAPS 

UMAPS offers African scholars drawn from across Africa the opportunity to spend five months at the University of Michigan, working and interacting with faculty members who are leaders in their fields. Each year, applications for the fellowship open on 15 August and close on 15 October. The programme started in 2009, hosting a single cohort each year. From 2020, the programme hosted two cohorts of about 15 African scholars each. These scholars are selected annually from an application pool of about 600. 

"It was an amazing experience, one that I wish all of my colleagues in the faculty and the institution at large could have," Dr Guvuriro said. “Other than meeting the faculty staff at the University of Michigan – who are amazing – I got to meet and interact with world leaders in the economics subdiscipline of my interest.” 

He concluded by stating that this is a rare opportunity for scholars, and although competitive, he believes it is worth applying for. “Although I was the first from the UFS to attend, I know that the August to December 2022 cohort has another UFS staff member, which is great. My wish would be for our university to be represented annually.”

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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