Rate My Professor Moi Hoon Yap

Professor Moi Hoon Yap serves as Professor of Image and Vision Computing in the Faculty of Science and Engineering at Manchester Metropolitan University, where she leads the Human-Centred Computing research theme in the Department of Computing and Mathematics. She earned her PhD in Computer Science from Loughborough University in 2009, MSc in Information Technology from Universiti Putra Malaysia in 2001, and BSc (Hons) in Statistics in 1999. Throughout her career, Yap has obtained substantial research funding from The Royal Society, EU, EPSRC, Innovate UK, Cancer Research UK, and industry partners. She held The Royal Society Industry Fellowship from 2016 to 2018, hosted by Image Metrics Ltd, and a PhD studentship from 2018 to 2022. Yap contributes to the field as Associate Editor of Computers in Biology and Medicine and as a panel member for UK funding bodies. Her appointments include Academic Lead for a KTP grant with C-Sols.

Yap's research focuses on computer vision and deep learning for medical image analysis, facial analysis, human behavioural analysis, and early disease detection, including diabetic foot ulcers, skin cancer, and breast lesions. She has pioneered projects such as FootSnap, a cloud-based infrastructure for diabetic foot ulcer detection; MAMMOBOT, a robot for early breast cancer diagnosis; and international grand challenges on diabetic foot ulcers and micro-expressions, creating novel datasets for reproducible research. Key publications encompass 'Deep learning in diabetic foot ulcers detection: a comprehensive evaluation' (Computers in Biology and Medicine, 2021), 'Analysis of the ISIC image datasets: Usage, benchmarks and recommendations' (Medical Image Analysis, 2022), 'AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images' (IEEE Transactions on Medical Imaging, 2022), 'Diabetic Foot Ulcer Grand Challenge 2022 Summary' (2023), and 'Diabetic Foot Ulcer Grand Challenge 2024: Overview and Baseline Methods' (2025). Her innovations, including smart ulcer detection technology, aim to reduce amputation rates and healthcare costs, showcasing significant impact in healthcare through sustainable software and point-of-care solutions.