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15 July 2022 | Story Lacea Loader

The Council of the University of the Free State (UFS) approved the lifting of the institution’s COVID-19 Regulations and Required Vaccination Policy with immediate effect.

“Since the declaration by the Government on 22 June 2022 that the COVID-19 regulations will be repealed, the UFS has conducted a risk assessment to determine the risk of exposure to staff and students. From the assessment, it was clear that the university’s COVID-19 infections are currently a low risk,” said Prof Francis Petersen, Rector and Vice-Chancellor of the UFS.

Factors that contributed to this low risk include the following:

  • No COVID-19 positive cases among UFS staff and students have been reported in the past month.
  • The high number of vaccinations among UFS stakeholders. In addition, the current national immunity level of the total South African population is high.
  • Certain faculties and postgraduate students are currently proceeding with hybrid/online learning, which minimises the risk of possible COVID-19 infections on the university’s three campuses.
  • In its correspondence of 23 June 2022, the UFS urged all staff and students to continue wearing masks should they have comorbidities and/or symptoms of illness, thus safeguarding other stakeholders.

“We believe that COVID-19 no longer poses an immediate threat to the safety of our staff and students, and that the pandemic is at a stage where they should take responsibility for their own safety. This can be mainly ascribed to the success of the implementation of the policy. Staff and students who still wish to wear masks are urged to do so at their own discretion. Those who have not yet been vaccinated against the virus and have no

known condition preventing them from doing so, are advised to get vaccinated for their own safety and protection,” said Prof Petersen.

The UFS COVID-19 Regulations and Required Vaccination Policy was approved by the University Council on 26 November 2021 and implemented on 6 December 2021. The university commenced restricting unvaccinated individuals from accessing its campuses as of 14 February 2022.

“If the national regulatory environment with respect to COVID-19 is changing to such an extent that the policy needs to be re-implemented, the university’s executive management will act accordingly, and hence the COVID-19 Regulations and Required Vaccination Policy remains a policy of the university as approved by the UFS Council on 26 November 2021.” said Prof Petersen.

 

Uplifting of the COVID-19 vaccination policy - mitigation strategies of the University of the Free State.

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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