Fumiya Inaba MSc

I am currently a PhD candidate in the Interdisciplinary Oncology Program (IOP) at the BC Cancer Research Institute (BCCRI), working with Drs. Martial Guillaud and Calum MacAulay. We work with medical images, primarily of histology and cytology slides. Through image processing, charactersitics of individual cells are measured and analyzed to extract clinically relevant insights of the patient. A priority for my research is translating complex data into clinically meaningful insights, ultimately improving patient outcomes and healthcare efficiency in three ways: early cancer detection, reducing overtreatment and developing prognostic tools. I obtained my Bachelor's degree in Medical Laboratory Sciences at the University of British Columbia, where I developed a keen interest in the applications of machine learning and data science in the pathology field, ultimately leading me to my current position. 

My current research aims to advance the understanding of cancer progression and its underlying biology through computational methods. Advanced algorithms, such as instance-segmentation of nuclei in whole slide images, enable a detailed analysis not only at the level of individual cells but also of their interactions and architectural distribution in tissue sections. Ultimately, my goal is to contribute to the development of a 3D simulation model that illustrates how cancer may progress within tissues or organs, depending on specified parameters.

The increasing availability and digitization of medical data have enormous transformative potential in the advancement of digital pathology research. However, to fully realize this potential, there must be a structured framework to manage the vast amount of data effectively. Through DASH, I aspire to contribute not only to the development of such frameworks but also to projects that will make data and its analyses more robust, scalable, and reproducible.

Research Interests: Digital pathology, Image processing, Machine learning, Deep Learning, Statistical analysis, Histopathology, Oncology, Spatial biology