Mathematics in Advanced Medical Imaging: models, algorithms, and big data

2019-12-30 ~ 2020-01-03 4243

The week-long workshop will gather together internationally leading investigators, early-career researchers, and trainees to communicate and review the recent advances in the fast-evolving field of medical imaging, and the impact of advanced applied mathematics on the current and future medical imaging field. Advanced mathematics such as geometry, calculus of variations, wavelets, compressive sensing and sparse representations, and deep learning has always played a key role in the development and enabling of advanced medical imaging technologies of clinical utility. As significantly new landscapes are emerging in the field of medical imaging in the past decade or so, they call naturally for new mathematical foundations and computational tools tailored to address the technological problems arisen in medical imaging. One of the new landscapes in medical imaging concerns the rapidly available tremendous amount of data of various types. It is recognized that there is an urgent need for mathematical and computational tools for handling, analysis, and annotation of these data and, more importantly, for assisting practitioners in their clinically useful interpretation and consumption of the data. In addition, the physics and technologies developed and/or optimized for acquiring medical imaging data are advancing rapidly as the result of powerful detectors, electronics, and computers are becoming readily available commodities. As always, applied mathematical tools would be a necessarily vital component empowering medical imaging technologies by being involved in data models, image creation/processing, and image utilization. As an example, the development of advanced medical tomographic imaging technologies tailored to address cancer screening, precision therapy, and treatment assessment has benefitted tremendously from the advances of optimization theory and algorithms in the field of applied and computational mathematics. There exists a need for a strong synergistic communication and collaboration between investigators

Organizers

Ke Chen(University of Liverpool)

Chunming Li (University of Electronic Science and Technology of China)

Dimitris Metaxas(Rutgers University)

Yu-Ping Wang (Tulane University USA)

Xiaochuan Pan (University of Chicago)

Speaker List (Tentative)

 

NO.

English Name

Employer's Name in English

1

Noor Badshah

University of Engineering   and Technology, Pakistan

2

Ke Chen

University of Liverpool, UK

3

Chong Chen*

Chinese Academy of Sciences,   China

4

Leo Chen*

Cardiff University Dental   Hospital, UK

5

Seungryong Cho

Korea Advanced Institute of   Science and Technology

6

Moo Kyung Chung

University of   Wisconsin-Madison

7

Bin Dong

Beijing University, China

8

Jinming Duan

University of Birmingham, UK

9

Yu-Ping Duan

Tianjin University

10

Georges El Fakhri

Harvard Medical School

11

Jianfeng Feng *

Fudan University

12

Zhichang Guo

Harbin Institute of   Technology

13

Howard Halpern

University of Chicago

14

Jaroslaw Harezlak

Indiana University, USA

15

Huiguang He*

Chinese Academy of Sciences,   China

16

Tianzi Jiang

Chinese Academy of Sciences,   China

17

Dexin Kong*

Zhejiang University

18

Hiroyuki Kudo

Tsukubo University

19

Sanjeev Kumar

Indian Institute of   Technology

20

Tobia Lasser

Technical University of   Munich, Germany

21

Chunming Li

University of Electronic   Science and Technology of China

22

Haixia Liang

Xi'an Jiaotong-Liverpool   University, Suzhou

23

Chang-Hong Liang *

Guang-Zhou Hospital

24

Jian Lu

Shenzhen University

25

Hongbin Lu

Fourth Military Medical   University

26

Ronald Lui

Chinese University of Hong   Kong

27

Arvid Lundervold

University of Bergen

28

Dimitris Metaxas*

Rutgers University

29

Xuan-Qin Mou

XiAn Jiaotong University

30

Xiaochuan Pan

University of Chicago

31

Zhifeng Pang

Henan University

32

Arman Rahmim

University of British   Columbia

33

Jin Keun Seo

Yonsei University, Korea

34

Yuying Shi

North China Electric Power   University

35

Xuecheng Tai

Baptist University of Hong   Kong

36

Anis Theljani

University of Liverpool, UK

37

Yu-Ping Wang

Tulane University, USA   

38

Ke Wei

Fudan University, China

39

Shu-Xian Yang

Neusoft

40

Yufeng Zang

Hangzhou Normal University,   China

41

Tieyong Zeng

Chinese University of Hong   Kong

42

Li Zeng

Chongqing University

43

Heping Zhang

Yale University, USA

44

Tingting Zhang

University of Virginia, USA

45

Kevin Zhou

Chinese Academy of Sciences,   China

46

Lei Zhu

University of Science and   Technology of China, China

Group Photo

 

现代医学影像中的数学模型、算法和大数据研究A.jpg

现代医学影像中的数学模型、算法和大数据研究B.jpg