Meet Rashindrie: PhD Engineering and IT student

Rashindrie Perera moved to Australia from Sri Lanka in March 2020 and while it was strange to move to a foreign country and to be working online, she was able to make great connections with her supervisors and lab colleagues. Her research uses computer science and engineering to help improve patience care plans by quantifying infiltrating lymphocytes in tissue sections.

After completing my undergraduate studies at the University of Moratuwa in Sri Lanka, I found about Melbourne University through a Study Abroad session. As I kept listening to what former PhD graduates were saying, I got increasingly comfortable about moving to Australia to start my journey on a PhD at the University of Melbourne.

The first meeting I had with my supervisor was via Skype and hearing about his work in bioinformatics – I was confident that this was the place I wanted to be doing my PhD.

Bioinformatics is a fast-moving area with a massive influence on the medical field. We’ve seen and heard of amazing research findings that have helped our medical staff fast-forward patient care and it’s an honour to be a part of this kind of great cause. This kind of positive impact motivates me to continue with my PhD, even if the impact is small.

I am currently working on analysing gigapixel whole slide images to quantify the presence of a type of cells known as tumour infiltrating lymphocytes (TILs), immune cells that enter the tumour micro-environment to mediate an anti-tumour response. TILs have risen to become an important biomarker in multiple tumours due to their favourable correlations with treatment responses and patient survival. TILs provide insight into the patients' immune system and can be used to help determine treatment pathways such as immunotherapy.

Currently, TILs are assessed manually in a semi-quantitative, visual manner on digitized tissue sections. The manual reporting of TIL scores in digitized tissue sections is a labour-intensive task subjected to a degree of ambiguity and inter-observer variability. Consequently, there has been a focus on using deep learning techniques to provide computational TIL scores that can assist with clinical treatments.

My research is to develop a model to predict the TIL score for an image with minimal expert supervision and is part of a broader initiative aimed at developing an accessible method for clinicians to read and score TILs without a microscope. While it’s a challenging task, if solved it could be massively helpful in clinical settings. We hope this will help determine treatment options for patients more easily.

I discovered bioinformatics as an undergraduate student studying computer science and engineering – and I was completely blown away. This led me down the path of integrating knowledge from across disciplines like computer science and engineering with bioinformatics. Now, as I am on my way to completing my PhD, I’m amazed at what we can do when we combine different areas or ways of thinking to develop new technologies and processes. I hope my research can be a part of helping improve patient care and studying at the University of Melbourne has made it a worthwhile experience.

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