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Keynote Speakers


Keynote Speaker

Prof. Edwin Wang

University of Calgary, Canada


Speech Title: "From Health Genomics to Intelligent Precision Health"

Abstract: Cancer is the leading cause of death and the third largest burden in the healthcare system in the world. Each year, more than 15 million new cancer patients are diagnosed and 7-8 million people die from cancer in the world. Current precision oncology is focusing on cancer treatment, however, with some notable exceptions, improvements in overall survival and morbidity over the past few decades have been modest. Historical data suggest that early detection of cancer is crucial for its ultimate control and prevention. To meet the challenges of the surge in cancer cases in the future, it is envisioned that, besides the promotion of lifestyle changes, improving early diagnosis is the best strategy for reducing the impact of carcinogenesis.
Both genetic and environmental factors (e.g., pollution, lifestyle and so on) interact to induce cancer initiation, progression and metastasis. Therefore, we are aiming to combine the genome sequencing, imaging and electronic medical records of individuals to identify high-risk cancer individuals, ‘healthy lifestyle patterns’ for cancer prevention, and monitor high-risk cancer individuals for cancer early detection. To do so, we have complied a cohort which contains 5 million people whose medical records have been collected. Among them, 0.5 million people’ genomic information has been determined. We are developing new algorithms by applying machine learning and deep learning approaches to the cohort to meet the goals mentioned above.

Prof. Kiyoshi Hoshino

University of Tsukuba, Japan




Assoc. Prof. Simon Fong

University of Macau, China


Plenary Speaker

Assoc. Prof. Hu Han

Chinese Academy of Sciences, China



Abstract: Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. Despite tremendous progress in attribute learning in recent years, joint estimate of a wide variety of face attributes accurately and efficiently from a single face image remains a challenging problem due to the data imbalance, label noise, etc. In this talk, I will briefly review the representative approaches for face attribute learning and highlight some of our latest research work on facial attribute from the exterior to the interior. In particular, I will cover our face attribute learning approaches in terms of the feature representation methods and the classification models. In addition, we also extend face attribute estimation into a more general scope, i.e., from the exterior to the interior such as heart rate estimation from the face.