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09 March 2022 | Story Dr Cornelius Hagenmeier
International
Internationalisation professionals attending the Dialogue on Innovative Higher Education Strategies National Multiplication Training workshop at the UFS.


The University of Venda (Univen) and the University of the Free State (UFS) have been awarded a grant from the German Academic Exchange Service (DAAD) Dialogue on Innovative Higher Education Strategies (DIES) National Multiplication Trainings (NMT) programme to implement training on internationalisation for higher education leaders and managers. It is co-funded by the German Rectors’ Conference (HRK) and the two coordinating universities. Two emerging internationalisation managers, Mr Matome Mokoena (UFS) and Mrs Nontlanhla Ntakana (Univen), are coordinating the programme, which is supported by DAAD with 25 000 euros.   

Dr Segun Obadire (Univen) and Dr Cornelius Hagenmeier (UFS), who serve as directors responsible for the international offices at their universities, are part of the training committee. The theme of the training programme is ‘Enabling Internationalisation in Light of the 2020 Policy Framework for Internationalisation of Higher Education in South Africa 2022’; it comprises two training workshops and several virtual engagements. The first training workshop was held at the UFS from 1 to 3 March 2022. 
 
Trendsetters

Mrs Nontlanhla Ntakana and Mr Matome Mokoena are alumni of the biannual DAAD DIES Training Course on Management of Internationalisation (MOI) at the Leibniz University Hannover in Germany. They seized the opportunity to forge a multiplication training that would impact internationalisation leaders and managers from across South Africa and empower them to leverage the 2020 Policy Framework for Internationalisation of Higher Education in South Africa to advance the internationalisation process at their institutions.

Internationalisation experts

Dr Nico Jooste and Mrs Merle Hodges served as external experts on the training committee. Both are internationally renowned experts in the field and former presidents of the International Education Association of South Africa (IEASA). Mr Leolyn Jackson (Central University of Technology, CUT) and Prof Lynette Jacobs (UFS) also contributed to the first training workshop.

Structure

This programme commenced in February, with participants engaging in topical readings and submitting their first assignment. First, a virtual workshop introduced participants to the UNIVEN Moodle e-learning platform used for the course. The face-to-face workshop at the UFS will be followed by a second in-person training at the University of Venda in September 2022. Virtual workshops and support of the participants through a dedicated WhatsApp group and other mentorship programmes will ensure the continuity of the training between the face-to-face workshops. Participants who were unable to attend the UFS and UNIVEN workshops in person could participate via a virtual link, thus ensuring that no participant is left behind. 

Participants

Twenty participants from eight public higher education institutions were selected by the training committee to participate in the training programme. Two participants from this year’s NMT cohort were also accepted into the DIES MOI course at the Leibniz University Hannover in Germany.  They are Prof Nontokozo Mashiya from the University of Zululand (Unizulu) and Mbali Mkhize from the Mangosuthu University of Technology (MUT).  Participants in the first workshop have indicated that they gained a lot from the numerous exercises and activities in the programme. They also mentioned that the programme would change the outlook of internationalisation at their universities in the future.                                                                                                              
                                            

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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