Fumiya Inaba

Digitization of medical data has allowed for increasingly complex analyses, offering an unprecedented depth of pathological insights for better informed medical care. As a graduate student in Interdisciplinary Oncology at BC Cancer, this is precisely where my research interests lie.

My current research work leverages a combination of deep learning, supervised and unsupervised machine learning methods to analyze digital pathology images on a cell-by-cell basis under the co-supervision of Dr. Martial Guillaud and Dr. Calum MacAulay. We aim to develop prognostic tools to improve or supplement current prognostic methods in prostate and breast cancer.

In my academic career thus far, I have given poster presentations at conferences such as Canadian Cancer Research Conference, B.I.G (Bioinformatics, Interdisciplinary Oncology, Genome Sciences and Technology), Pathology day, given oral presentations at BC Cancer Summit (2023), TCAIREM summer trainee sessions, and been awarded with the Canada Graduate Scholarships - Master's to supplement my graduate studies.