Alexander Baine presented the team’s research on gliomas—aggressive brain tumours where accurate diagnosis is essential for treatment planning, highlighting both the technical and biomedical implications of their work.

Building on a strong foundation of in-house research in computer vision and digital pathology, the team leveraged advanced machine learning techniques to develop a robust, generalizable solution. Their model demonstrated exceptional performance in identifying abnormal mitoses, a key factor in glioma grading and prognosis.

 Reflecting on the experience, the team said, “Participating in the Glioma-MDC challenge pushed us to apply our research in a highly practical, clinically relevant context. It was incredibly rewarding to work through the problem sharing different ideas whilst focused on a single challenge with real-world impact.”

Their success underscores the vital role of interdisciplinary AI research in transforming medical diagnostics and patient care.