Latest News Archive

Please select Category, Year, and then Month to display items
Previous Archive
28 January 2021 | Story Dr Nitha Ramnath | Photo Sonia Small
Prof Phillippe Burger.

The COVID-19 pandemic has disrupted the entire world, claiming more than two million lives and sparing no region. The world is confronted with urgent unsolved challenges, with the poor and vulnerable populations, low-skilled workers, and refugees most affected. 

These challenges will be addressed by the Lancet COVID-19 Commission and its various task forces, one of which is the Fiscal Policy and Financial Markets task force. Prof Philippe Burger, Professor of Economics and Pro-Vice-Chancellor: Poverty, Inequality and Economic Development at the University of the Free State, serves as a member of the commission’s Fiscal Policy and Financial Markets task force. The eleven members of the task force include two Nobel prize laureates in economics, as well as academics and public-policy specialists from across the world, under the co-chairpersonship of Dr Vitor Gaspar (Director of the Department of Fiscal Affairs at the IMF) and Prof Felipe Larraín (Professor of Economics, Pontifical Catholic University of Chile and former Minister of Finance of Chile).

The commission is an interdisciplinary initiative across the health sciences, business, finance, and public policy, and was created to help speed up global, equitable, and lasting solutions to the pandemic. The work of the commission is divided into 12 task forces, each composed of members from diverse disciplinary interests, geographies, and identities. These task forces provide support in areas ranging from vaccine development to humanitarian relief strategies, to safe workplaces, to global economic recovery. 

Key aims of the commission is to speed up awareness and the worldwide adoption of strategies to suppress transmission, as well as to ensure that COVID-19 vaccines and key technologies are equitably accessible across the world.

The Fiscal Policy and Financial Markets task force will consider fiscal and financial issues related to the pandemic affecting advanced, emerging market, and developing economies. Based on evidence and best practices, the task force will provide recommendations on managing the effects of the pandemic and will also manage the transition to a resilient, smart, inclusive, and green growth path. Issues related to fiscal sustainability as well as debt relief in poor countries are on the task team’s agenda.

Many multilateral institutions such as the WHO, the IMF, the World Bank, the Food and Agricultural Organisation of the UN, the UN World Food Programme, the UN Educational, Scientific and Cultural Organisation, the Organisation for Economic Co-operation and Development, and others face profound challenges in undertaking their crucial missions to coordinate the global response to the pandemic. The Lancet COVID-19 Commission also aims to make recommendations to strengthen the efficacy of these critical institutions. Moreover, the commission reaches out to regional groupings, including the African Union, the Association of Southeast Asian Nations (ASEAN), the Southern Common Market (MERCOSUR), and others, to support the efforts of these bodies in fighting the pandemic. 

The Lancet COVID-19 Commission and its task teams include leaders in health science and healthcare delivery, business, politics, and finance from across the world. They volunteer to serve in their individual capacities – not as formal representatives of their home institutions – and will work together towards a shared and comprehensive outlook on how to stop the pandemic and how best to promote an equitable and sustainable recovery. 

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