AI in Medical and Biological Data Applications

Prof. Yi Pan

Chair Professor and Dean Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technologies, China and Regents’ Professor Emeritus Georgia State University, USA


Artificial Intelligence (AI) is the science of mimicking human intelligences and behaviors. Machine Learning (ML), a subset of AI, trains a machine how to use algorithms or statistics to find hidden insights and learn automatically from data. Deep learning (DL) is one of machine learning methods where we use deep neural networks with advanced algorithms such as auto-encoding or convolution to recognize patterns in data. AI has become very successful recently due to the availability of huge data and powerful supercomputers. Many applications such as speech and face recognition, image classification, natural language processing, bioinformatics, health informatics such as disease prediction and detection suddenly took great leaps due to the advance of AI. Although various AI architectures and novel algorithms have been invented for many bio and health applications, better explainability, increasing prediction accuracy and speeding up the training process are still challenging tasks among others. In this talk, I will outline recent developments in AI research for bioinformatics and health informatics. The topics discussed include proposing more effective architectures, intelligently freezing layers, gradient amplification, effectively handling high dimensional data, designing encoding schemes, mathematical proofs, optimization of hyper-parameters, effective use of prior knowledge, embedding logic and reasoning during training, result explanation and hardware support. These challenges create a huge number of opportunities for people in both computer science and health care. In this talk, some of our solutions and preliminary results in these areas will be presented and future research directions will also be identified.


Dr. Yi Pan is currently a Chair Professor and the Dean of Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China and a Regents’ Professor Emeritus at Georgia State University, USA. His current research interests mainly include bioinformatics and health informatics using big data analytics, cloud computing, and machine learning technologies. He has published more than 450 papers including over 250 journal papers with more than 100 papers published in IEEE/ACM Transactions/Journals. In addition, he has edited/authored 43 books. His work has been cited more than 19000 times based on Google Scholar and his current h-index is 88. He has served as an editor-in-chief or editorial board member for 20 journals including 7 IEEE Transactions. He is the recipient of many awards including one IEEE Transactions Best Paper Award, five IEEE and other international conference or journal Best Paper Awards, et al. He has organized numerous international conferences and delivered keynote speeches at over 70 international conferences around the world. Dr. Pan is a Member of the Academy of the United Nations Sciences and Technology Organization, Foreign Member of Ukrainian Academy of Engineering Sciences, Fellow of American Institute for Medical and Biological Engineering, Fellow of Institute of Engineering Technology and Fellow of Royal Society for Public Health.

Intelligent Infant Brain MRI Analysis and Early Brain Development Study

Prof. Dinggang Shen

School of Biomedical Engineering, ShanghaiTech University

Shanghai United Imaging Intelligence Co., Ltd.


The increasing availability of infant brain MRI data affords unprecedented opportunities for precise charting of dynamic early brain developmental trajectories in understanding normative and aberrant brain growth. However, most existing neuroimaging analysis tools were mainly developed for adult brains, and thus are not suitable for infant brains with extremely low tissue contrast, dynamic appearance changes, and fast developing shape/folding. In this talk, I will introduce our over 10 years of experience in developing learning-based computational analysis methods for quantitative characterization of early brain development, as well as reconstruction of dynamic brain structural and functional development atlases. Neuroscience applications of these methods in advancing our understanding of infant brains will be also introduced.


Dinggang Shen is a Professor and a Founding Dean with School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, and also a Co-CEO of United Imaging Intelligence (UII), Shanghai. He is a Fellow of IEEE, The American Institute for Medical and Biological Engineering (AIMBE), The International Association for Pattern Recognition (IAPR), and The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society. He was Jeffrey Houpt Distinguished Investigator and a Full Professor (Tenured) with The University of North Carolina at Chapel Hill (UNC-CH), Chapel Hill, NC, USA, directing The Center of Image Analysis and Informatics, The Image Display, Enhancement, and Analysis (IDEA) Lab, and The Medical Image Analysis Core. His research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 1500 peer-reviewed papers in the international journals and conference proceedings, with H-index 123 and 60000+ citations. He serves as an Editor-in-Chief for Frontiers in Radiology, as well as an editorial board member for eight international journals. Also, he has served in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, in 2012-2015, and was General Chair for MICCAI 2019.

AI for Medical Imaging Informatics – Where have We Missed Explainability

Professor KC Santosh

the Chair of the Department of Computer Science (CS) at the University of South Dakota (USD)


When we consider AI for healthcare, infectious disease outbreak is no exception. The talk will begin with machine learning models that help in not only predicting but also detecting abnormalities due to infectious diseases such as Pneumonia, TB, and Covid-19. I will open my talk with infectious disease prediction models and unexploited data, where we will learn that predictive analytical tools are close to garbage-in garbage-out (at least for Covid19). I will then cover multimodal learning and representation based on both shallow learning (handcrafted features) as well as deep learning (deep features) that typically apply on medical imaging tools. Like in computer vision, I will open an obvious question, how big data is big in addition to common techniques data augmentation and transfer learning. With all these facts, as most of models are limited to education and training, I will end up my talk with the statement “ML innovation should not be limited to building models.” What we need is ExplainablableAI in ActiveLearning framework.


Professor KC Santosh is the Chair of the Department of Computer Science (CS) at the University of South Dakota (USD). Prior to that, he worked as a research fellow at the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). He worked as a postdoctoral research scientist at the LORIA research center, Université de Lorraine in direct collaboration with industrial partner ITESOFT, France. He also served as a research scientist at the INRIA Nancy Grand Est research center (France), where he received his PhD in Computer Science – Artificial Intelligence.  His research projects, primarily in Applied AI, are funded (of more than $2m) by multiple agencies, such as SDCRGP, Department of Education, National Science Foundation, and Asian Office of Aerospace Research and Development. He has demonstrated expertise (with 10 books, 220+ research articles, and 20+ journal edited issues, as of Dec. 2021) in artificial intelligence, machine learning, pattern recognition, computer vision, image processing, and data mining with applications such as medical imaging informatics, document imaging, biometrics, forensics, and speech analysis. He completed leadership and training programs for Deans/Chairs (organized by the Councils of Colleges of Arts & Sciences (U.S. 21)) and PELI – President’s Executive Leadership Institute (USD 21). He is highly motivated/interested in academic leadership. To name a few, Prof. Santosh is the proud recipient of the Cutler Award for Teaching and Research Excellence (USD 2021), the President’s Research Excellence Award (USD 2019) and the Ignite Award from the U.S. Department of Health & Human Services (HHS 2014).