Latest News Archive

Please select Category, Year, and then Month to display items
Previous Archive
13 July 2023 | Story Andre Damons | Photo Samkelo Fetile
Prof Catherine Comiskey
Prof Catherine Comiskey, a professor in Healthcare Statistics from the School of Nursing and Midwifery at Trinity College Dublin and Academic Director of CHARM-EU, presents a lecture on building a research career with global impact to members of the UFS Transformation of the Professoriate Mentoring Programme.

A visiting scholar from Trinity College Dublin in Ireland visited the University of the Free State (UFS) to work with staff members from the UFS Transformation of the Professoriate Mentoring Programme on identifying collaborations, writing, and building a research career.

Prof Catherine Comiskey, a professor in Healthcare Statistics from the School of Nursing and Midwifery at Trinity College Dublin and Academic Director of CHARM-EU – an EU-funded academic programme – held a writing retreat for participants in the Transformation of the Professoriate Mentoring Programme in the last week of June. She also worked with individual members to identify potential European and UK collaborators on various research projects. On Friday 30 June, she presented a lecture on building a research career with global impact.

Encouraging staff members

According to Dr Henriëtte van den Berg, Manager: Transformation of the Professoriate Mentoring Programme, Prof Comiskey encouraged colleagues to develop a research and publication strategy to ensure that they optimise the work they are doing, to look for opportunities to collaborate with colleagues across different disciplines, and to work together on publications and the supervision of postgraduate students.

“She also emphasised the importance of collaborating with people in industry, as they often have a rich source of data that is publishable. She highlighted the importance of being an ethical researcher. The workshop participants benefited from her passion and broad knowledge to start planning collaborations and to reflect on how they can make the work they are already doing work more for them. A group of workshop participants has already started working on a systematic review that they will conduct in collaboration with Prof Comiskey,” said Dr Van den Berg.

Share expertise

Prof Comiskey facilitated online writing interventions for the colleagues of the mentoring programme during COVID-19 lockdown restrictions. She was invited to the campus to share her expertise in quantitative methodology and transdisciplinary work.

Prof Comiskey completed a PhD in Mathematics and coordinates many interdisciplinary research teams, comprising applied mathematicians, statisticians, psychologists, medical doctors, sociologists, anthropologists, nurses, computer scientists, and healthcare employees. She has been selected as one of five international experts nominated by the European Commission to serve on the International Scientific Committee of the European Monitoring Centre for Drugs and Drug Addiction.

She has 30 years’ experience of teaching, research, postgraduate supervision, and teaching to specialists and non-specialists in all areas of applied statistics, mathematics, and epidemiology. She is also a seasoned academic leader, having served as Research Director at Trinity College, Dublin for many years.

CHARM-EU is an EU-funded academic programme spanning five European universities to develop, run, and evaluate a new EU-wide model for Universities of the Future. This involves a new transdisciplinary master’s degree that addresses the Sustainable Development Goals (SDG).  

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