Tensymp 2020


Mohammad Faizal Ahmad Fauzi, PhD

Associate Professor
Faculty of Engineering, Multimedia University, Malaysia

Short Bio:

Mohammad Faizal Ahmad Fauzi (M’00, SM’10) 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 an Associate Professor at the Faculty of Engineering, Multimedia University. His main research interests are in the area of signal and image processing, pattern recognition, computer vision and medical imaging. From May 2013 to June 2014, he was attached to the Clinical Image Analysis Lab at the Ohio State University, USA where he worked on digital histopathology, especially on cancer and diseases analysis. He has published more than 80 journal and conference articles to date. Mohammad Faizal is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE), which he first joined in 2000. He is currently serving as the Chair for IEEE Region 10 Newsletter Committee, Past Chair of the IEEE Malaysia Section as well as the Advisor for the IEEE Signal Processing Malaysia chapter.


Tumor Budding Detection in Colorectal Cancer from Whole-Slide Pathology Images


Tumor budding is defined as the presence of single tumor cells or small tumor clusters (less than five cells) that ‘bud’ from the invasive front of the main tumor. Tumor budding (TB) has recently emerged as an important adverse prognostic factor for many different cancer types. In colorectal carcinoma (CRC), tumor budding has been independently associated with lymph node metastasis and poor outcome. Pathologic assessment of tumor budding by light microscopy requires close evaluation of tumor invasive front on intermediate to high power magnification, entailing locating the ‘hotspot’ of tumor budding, counting all TB in one high power field, and generating a tumor budding score. By automating these time-consuming tasks, computer-assisted image analysis tools can be helpful for daily pathology practice, since tumor budding reporting is now recommended on select cases. In this talk, we will present our work on the development of a tumor budding detection system in CRC from whole-slide Cytokeratin AE1/3 images, based on de novo computer algorithm that automates morphometric analysis of tumor budding.