Addressing Towards Precision Medicine: Artificial Intelligence in Digital Pathology for Cancer Treatment Recommendation

Prof. Ir. Dr. Mohammad Faizal Ahmad Fauzi received the B.Eng. degree in Electrical and Electronic Engineering from Imperial College, London, UK in 1999, and the Ph.D. degree in Electronics and Computer Science from University of Southampton, Southampton, UK in 2004. He is currently a Professor at the Faculty of Engineering, Multimedia University, and the Head for the MMU-UKM-IMU IMU Artificial Intelligence for Digital Pathology (AI4DP) Research Excellence Consortium. His main research interests are in signal and image processing, machine learning, medical imaging, and biomedical informatics. He has published more than 100 journal and conference articles to date and delivered invited and keynote lectures at many international conferences. From May 2013 to June 2014, and April to June 2017, he was attached to the Ohio State University Wexner Medical Center as a visiting scholar where he worked on cancer diagnosis and prognosis in digital pathology. Mohammad Faizal is a Chartered Engineer (CEng) with the Engineering Council UK, and a Professional Engineer (PEng) with the Board of Engineers Malaysia. He is also a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) which he volunteers actively. He is a recipient of many awards such as the 2017 Fulbright-MCMC Senior Researcher, 2020 IEEE SPS Meritorious Regional/Chapter Service Award and the 2021 IEEE Region 10 Outstanding Volunteer Award..

Abstract: Pathology is a branch of medical science primarily concerning the examination of organs, tissues, and bodily fluids to make a diagnosis of disease, especially cancers. For most types of cancer, pathology remains the ‘gold standard’ for the diagnosis of cancer. Digital pathology is the management and interpretation of pathology information in a digital environment that enables better, faster and cheaper diagnosis, prognosis and prediction of cancer and other important diseases. With the advent of whole slide imaging, the field of digital pathology has gained great attention and is currently regarded as one of the most promising avenues of diagnostic medicine. Hormone receptor status is determined primarily to identify breast cancer patients who may benefit from hormonal therapy. The current clinical practice for hormone receptor testing, using either Allred score or H-score, is still based on laborious manual counting and estimation of the proportion (P-score) and intensity (I-score) of positively stained cancer cells in immunohistochemistry (IHC)-stained slides. This manual process is not only tedious and time-consuming but is also prone to errors and inaccuracies. In this talk we will present our work in developing an AI-based computer-aided diagnosis system for the scoring of tumor biomarkers in digital pathology slides for breast cancer treatment recommendation. The developed system has the potential to improve the overall standards of prognostic reporting for breast cancer by minimizing errors and inaccuracies, eliminating sampling bias and reader variability, providing faster and more consistent reporting, as well as reducing pathologists’ workload.