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03 March 2022 | Story Dr Nitha Ramnath | Photo istock
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The University of the Free State (UFS) has joined The Conversation Africa (TCA) as a funding partner.  TCA, a not-for-profit media initiative, is part of a global platform that publishes articles written by academics and researchers.  The platform’s objective is to make the knowledge produced in the academy accessible, easy to understand, and freely available to the general public. Articles are published daily on the TC-Africa website - https://theconversation.com/africa. 

The platform uses a Creative Commons republishing model. This means articles can be republished by other media on the continent and internationally, ensuring even greater reach to audiences including academics, policy makers, funders, and the general public. 

To date, more than 55 UFS researchers and academics have published with TCA, and their articles have garnered more than 1,3 million readers globally. UFS researchers and academics are encouraged to publish with The Conversation. 

As part of the partnership, TCA will run writing workshops for UFS academics and researchers who want to enhance their writing and science communication skills. Dates for these will be announced soon.

How you can publish with The Conversation Africa

• Engage with The Conversation Africa editors when they contact you directly to write about your research area and expertise. The articles are short, ± 800 to 1 000 words.

• Pitch your idea for an article directly to The Conversation Africa here   

• Register as an author, and set up a profile

• Engage with the Communication and Research offices. Every week, The Conversation Africa sends an expert request for expert authors on topical issues to the Communication and Research offices, which can identify researchers. 
- Interested researchers are put into contact with the relevant editor at The Conversation to discuss the potential article

Why should you get published on The Conversation Africa?

Benefits for researchers and academics:

• Articles on the platform help to raise the profile of academics, often leading to policy engagement with governments, businesses, industry or professional bodies, conference invitations, academic collaborations, and further media exposure. 
• In the course of writing, academics get bespoke editorial assistance from the team working in consultation with them. 
• The opportunity to take part in a hands-on science communication writing workshop.
• Readership and republication metrics for each published article.
• A global readership with up to 1,2 million readers monthly.

Benefits for Communication and Marketing and the Research office:

• Provides well-curated, ready-to-use communication material for websites and social media. 
• Helps to profile the work of the university for marketing, communication, and awareness.
• Provides media exposure to the talent pool of UFS academics and researchers. 

Benefits for and across the university:

• Shines a spotlight on the excellent research and innovation at the UFS.
• Demonstrates the UFS’ commitment to facilitating greater engagement with society and promoting interdisciplinary communications.
• Visibility for the institution and researchers nationally and globally.
• Access to institutional analytics, including detailed data on the content published by UFS researchers.

Contact The Conversation Africa:

To arrange departmental meetings and introductory sessions to The Conversation Africa team, contact: Pfungwa Nyamukachi, Strategic Partnerships and Stakeholder Relations Manager: pfungwa.nyamukachi@theconversation.com 

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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