Rashindrie Perera
- cdperera@student.unimelb.edu.au
- Room 301, Block E, Building 170
Thesis Title
Deep Learning for Fast and Accurate Cancer Prognostication using Medical Images
Research overview
Medical images capture important information for tumour prognostication. Especially, the quantity of Tumour Infiltrating Lymphocyte (TIL) cells is increasingly recognised as a reproducible biomarker that can inform prognosis and predict treatment response. However, the routine manual assessment of TILs on large tissue sections is a tedious task subject to a degree of ambiguity and interobserver variability. Although best practices in manual TIL assessment attempt to address some of these issues, there still exist several challenges that cannot be fully resolved through such standardisation. Our research aims to address these challenges by developing machine learning-based approaches for TIL assessment.
Supervisors
Qualifications
B.Sc. (Hons) Computer Science & Engineering, University of Moratuwa, Sri Lanka (2018)