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12 December 2020 | Story André Damons | Photo Supplied
Read More Bianca Vermeulen
Bianca Vermeulen started her journey to become a doctor this year after being accepted by the University of the Free State (UFS) to study medicine. She had previously applied 32 times in eight years to study medicine.

A first-year medical student from the University of Free State (UFS) is finally on her way to realise her childhood dream of becoming a doctor after having been rejected 32 times in eight years to study medicine.

Bianca Vermeulen, who started the MBChB programme in 2020, said she applied 32 times in eight years and got rejected every time. As a qualified Critical Care Clinical Technologist who worked for the Free State Department of Health, the daily interaction with her patients and colleagues inspired her to keep her dream alive.

“My childhood dream (of becoming a doctor) did not fade. Dreams do not have expiry dates. During my time in the clinical setting, I learnt some important life lessons. Experience is most definitely what I got when I did not get what I wanted,” said Vermeulen.

According to her, working in a clinical setting fueled her passion. Said Bianca: “I woke up to an alarm clock of opportunity. At the end of the day I can go home with a feeling of satisfaction. I could not have done it without the support of my colleagues and friends. Then it all becomes worth it.”

Finally, a yes to study medicine

Vermeulen said she was at work when she received an e-mail on 3 October 2019 from the UFS application office. She initially ignored the e-mail thinking they would resend one of their earlier rejection letters. After ‘accidentally’ opening the letter, she could not believe her eyes.

“For a moment I was in denial. I had to read the letter a few times to ensure my eyes were not bewitching me. I had to show a friend to ensure that I had read and understood the letter. Then the reality came as an overwhelming mixture of emotions.”

Studying medicine during a pandemic

Vermeulen , who has a passion for neonatal and paediatric intensive care and would like to specialise in paediatrics and child health care after her undergraduate studies, said she welcomes the change that COVID-19 brought to the academic table.

“Daily routine changed overnight for all people and all stared uncertainty in the face. Students had to adapt to a blended learning approach (which also had its own challenges), but as time progressed, we learnt the new ropes.

“I truly hope that we all take the COVID lessons to heart. In the medical sector, no one is a greater ‘hero’ than another. The sector needs various role players and I hope that people realise the importance of nurses, hospital cleaners, administrative staff and all allied health workers. Without these people, the medical sector cannot function. We all need one another.

“With that being said, I hope people realise that we need a functional system so that we can work with each other and not against a system,” said Vermeulen.

Working with various healthcare workers, she has seen the effects of burnout and experienced the best (and worst) of both worlds but is still happy with her choice to study medicine.

It only takes one successful application

“As [US educator] Randy Pausch said: ‘The brick walls are there for a reason. The brick walls are not there to keep us out. The brick walls are there to give us a chance to show how badly we want something.’ I take this to heart,” Vermeulen said.

“You might have received ample unsuccessful applications, but it will only take one successful application to commence with your dream. If it is truly something you want to do, never give up on your dreams. Always work hard and take to heart what the Lord has done for you!”

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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