Title: Digital Pathology for Non-Alcoholic Fatty Liver Disease Diagnosis

Abstract: AI in medicine has become a popular and important interdisciplinary research area. Around 30% of adults worldwide are suffering different degrees of liver diseases, e.g. fatty liver and hepatitis which may further progress to liver cancer. Liver cancer is also the leading cause of cancer death in the world and the second most common cause of cancer death in the Asia-Pacific region. In this talk, I will share my research work on liver biopsy image analysis for non-alcoholic fatty liver disease. Specifically, I would like to share two recent research works for determining fibrosis staging and non-alcoholic fatty liver disease activity score from liver biopsy images.  

Prof. Pong Chi YUEN received his Ph.D. degree in Electrical and Electronic Engineering from the University of Hong Kong. He joined Hong Kong Baptist University in 1993 and served as the Head of the Department of Computer Science from 2011 to 2017. Currently, he is a Chair Professor in Computer Science and Associate Dean of the Science Faculty at Hong Kong Baptist University.

Over the years, Dr. Yuen has spent his sabbatical and visited some universities/research institutes as a visiting professor/scholar, including The University of Sydney (Australia), The University of Maryland at College Park (USA), INRIA Rhone Alpes (France), ETH Zurich (Switzerland), and The University of Bologna (Italy). Dr. Yuen was the director of Croucher Advanced Study Institute (ASI) on biometric authentication in 2004 and Croucher ASI on Biometric Security and Privacy in 2007. He has been the Director of IAPR/IEEE Winter School on Biometrics since 2017. 

Dr. Yuen has been actively involved in many international conferences and professional community as a general/program co-chair, including ICPR 2006, BTAS 2012, ISBA 2016, WIFS 2018, IJCB 2021. He served as Associate Editor of IEEE Transactions on Information Forensics and Security (2014 – 2018, 2021-2022), and received the Outstanding Editorial Board Service Award in 2018. Dr. Yuen has served as the Vice President (Technical Activities) of the IEEE Biometrics Council (2017-2019) and Associate Editor/Senior Editor of the SPIE Journal of Electronic Imaging (2012-2019). Currently, Dr. Yuen is the Senior Area Editor of IEEE Transactions on Information Forensics and Security, Editorial Board Member of Pattern Recognition, and Associate Editor of IEEE Transactions on Biometrics, Behaviour and Identity Science. He received the first-prize and second-prize Natural Science Awards from the Guangdong Province and the Ministry of Education, China, respectively. He is a Fellow of IAPR.

Title: Image-based Primary Open-angle Glaucoma Diagnosis and Model Fairness

Abstract: Primary open-angle glaucoma (POAG) is one of the leading causes of blindness globally and in the US, potentially affecting an estimated 111.8 million people by 2040. Among these patients, 5.3 million may be bilaterally blind. POAG remains asymptomatic until it reaches an advanced stage, leading to visual field loss. However, early diagnosis and treatment can avoid most blindness caused by POAG. Therefore, accurately identifying individuals with glaucoma is critical to clinical decision-making. In recent years, developments in artificial intelligence have offered the potential for automatic POAG diagnosis and prognosis using fundus photographs. In this talk, I will review our research on image-based POAG diagnosis. I will also discuss how we are working to ensure model fairness across protected groups in deep learning models. Our proposed approach aims to alleviate concerns about the fairness and reliability of image-based computer-aided diagnosis.

Prof. Yifan Peng is an assistant professor in the division of Health Sciences at Weill Cornell Medicine. His main research interests include BioNLP and medical image analysis, such as named entity recognition, information extraction, and disease diagnosis and prognosis. Before joining Cornell Medicine, He was a research fellow at the National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH). He obtained his Ph.D. degree from the University of Delaware. During his doctoral training, he investigated applications of machine learning in biomedical relation extraction, with a focus on deep analysis of the linguistic structures of biomedical texts. He is the first awardee at the NCBI to receive the NIH K99/R00 grant, which supports his work on using NLP and ML to extract radiology specific domain knowledge and build an automated reporting system.

Title: Transcriptomic, histopathological and radiological deep learning models for cancer subtype classification

Abstract: It is now clear that major human cancers are heterogeneous diseases, which hamper the selection of patients for optimized clinical management and treatment. To dissect the complex cancer heterogeneity, molecular subtyping has been widely adopted as an effective strategy in the last couple of decades. Despite the insights into cancer biology, the conventional cancer subtyping studies were mostly based on conventional machine learning methods applied to transcriptomics data, which lack cross-platform robustness and are not suitable for clinical translation. In this talk, I will first introduce a deep learning framework based on transcriptomic profiles, which is platform independent, robust to missing data, and can be used for single sample prediction. To facilitate clinical implementation, I will next introduce our recent works on deep learning models for cancer subtyping based on histopathological images and radiological scans. These medical imaging data is widely available in the clinic, hence more suitable for the translation of cancer subtyping into real clinical practice to advance precision oncology.  

Prof. Xin Wang obtained his PhD in 2014 from the University of Cambridge Department of Oncology and Cancer Research UK Cambridge Institute. From 2013 to 2015, Prof. Wang did his postdoctoral research at the Department of Biomedical Informatics, Harvard Medical School. He is currently an Associate Professor at the Department of Surgery, Chinese University of Hong Kong, directing the Division of Biomedical Informatics and MPhil-PhD Programme in Translational Genomics. He is currently also leading the Laboratory of Translational Bioinformatics at LI Ka Shing Institute of Health Sciences as a Principal Investigator. 

Prof. Wang’s major research field is cancer genomics and bioinformatics. Since 2012, he has been focusing on mechanistic and translational research in major human cancers by developing novel methodologies integrating bioinformatics, systems biology, machine learning and artificial intelligence. In collaboration with molecular biologists and experimental oncologists, he is especially fascinated by the research into the mechanisms underlying cancer development and metastasis. Together with clinicians, his group has also been dedicating to multi-center studies about molecular and image-based biomarkers for cancer early detection, diagnosis, prognosis and subtyping. Prof. Wang published > 90 papers in well-known journals such as Nature Medicine, Gastroenterology, Hepatology, Annals of Surgery, Science Advances, and Nature Communications, with > 10,000 citations. His work is currently supported by significant research funds by Research Grants Council of Hong Kong, Shenzhen City and Guangdong Province, as well as National Natural Science Foundation of China.

Title: Application of data science technologies in the precision medicine of brain cancer

Abstract: Recent advancements in next-generation sequencing and data science are revolutionizing various areas of life science and medicine. I am dedicated to discovering and investigating genomic alterations that impact complex human diseases and relevant biological models by developing and applying computational methods based on statistics and machine learning, in order to bridge the gaps between data, bench, and bedside. While large-scale genome sequencing projects have uncovered the mutational landscapes of several cancers, how cancer cells evolve with and without therapy remains unclear. My lab aims to address long-standing questions regarding how cancer cells respond to therapy and how the founding somatic alterations drive cancer evolution trajectories. In this talk, I will present the latest research from my laboratory on cancer evolution and precision medicine in the study of brain tumors.

Prof. Jiguang Wang received his Ph.D. in Applied Mathematics from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS). Between 2011 and 2015, he was a Postdoctoral Research Scientist at Columbia University. In 2015, he was named the Precision Medicine Fellow and promoted to Associate Research Scientist. He established the Wang Genomics Laboratory @HKUST in 2016, focusing on the application of data science and AI in biology and medicine. 

Title: A Generalized Label-efficient Framework for Medical Image Segmentation and a new Benchmark for Colorectal Cancer Segmentation

Abstract: Medical image segmentation plays a crucial role in clinical settings, augmenting the accuracy of diagnosis, treatment planning, and disease surveillance. Here we present a generalized weakly supervised framework for medical image segmentation, termed WeakMed, to alleviate the reliance on costly annotations while being tractable to various scenarios. Specifically, the proposed WeakMed method creatively utilizes the mask-to-box transformation to overcome rectangular shape biases inherent in bounding box labels by leveraging economical bounding box annotations, thus largely streamlining the annotation of medical data. Besides, WeakMed is a versatile model, enabling adaptation to a variety of segmentation tasks and bringing no computation costs to model inference. Through rigorous evaluation across 9 segmentation tasks, 10 diverse medical datasets, and 6 distinct imaging modalities, WeakMed demonstrates superior accuracy and robustness when compared to its fully/weakly supervised counterparts. By alleviating the data annotation burden associated with a broad spectrum of segmentation tasks, WeakMed has the potential to advance diagnostic accuracy and facilitate the development of personalized treatment strategies. Besides, we collected and annotated the first benchmark dataset that covers diverse ERUS scenarios, i.e. colorectal cancer segmentation, detection, and infiltration depth staging. Our ERUS-10K dataset comprises 77 videos and 10,000 high-resolution annotated frames.

Prof. Zhen Li is currently an assistant professor at the School of Science and Engineering (SSE) of The Chinese University of Hong Kong (Shenzhen)/Future Intelligent Network Research Institute (FNii)-Shenzhen. Dr. Zhen Li was selected for the Wu Wenjun Outstanding Youth of 2023, 2021-2023 Seventh China Association for Science and Technology Young Talent Support Project. His research interests include interdisciplinary research in artificial intelligence, 3D vision, computer vision, and deep learning-assisted medical big data analysis. He has published over 60 papers in top conferences and journals, such as top journals Cell Systems, Nature Communications, IEEE TPAMI, IEEE TNNLS, IEEE TMI, PLOS CB, Bioinformatics, JCIM etc., and top conferences CVPR, ICCV, ECCV, NeurIPS, ICLR, MM, AAAI, IJCAI, ACL, ECAI, MICCAI, RECOMB, etc.