Organiser: Fysikum, Stockholm University
No registration required

Abstract

Cancer, a global health crisis responsible for an estimated 10 million deaths just in 2020, necessitates innovative approaches to combat its devastating impact. Rapid progress in image acquisition and hardware technologies over the past three decades has resulted in a new era of medical imaging, allowing for the capture of high-resolution anatomical, physiological, functional, and metabolic data from cancerous organs. This capability has firmly embedded medical imaging into the clinical routines of oncology, from initial diagnosis and non/minimally invasive assessment of disease prognosis to treatment planning and image-guided radiation therapy.

However, the growing reliance on medical imaging has led to an overwhelming volume of scans, posing challenges for manual interpretation. The limitations of human analysis have spurred the development of computerized tools for automatic or semi-automatic image examination. Recent breakthroughs in artificial intelligence, particularly deep learning techniques, have transformed various sectors, including healthcare's radiology and radiotherapy fields. In this presentation, I will overview the applications of AI in cancer care, with a particular focus on the critical role of AI in cancer imaging.

Brief description of the speaker:

Mehdi Astaraki is a postdoctoral researcher at the Division of Medical Radiation Physics at SU with a passion for applying deep learning (DL) and machine learning (ML) methods to cancer imaging analysis. He aims to develop imaging biomarkers for diagnosis, prognosis of cancer stages, cancer development, treatment efficacy, and radiation treatment planning. He earned both his bachelor's and master's degrees in Biomedical Engineering. In September 2022, he earned his Ph.D. degree from the joint program between the Division of Biomedical Imaging at KTH and the Division of Medical Radiation Physics at KI.