This proposal is to organize a new workshop at TSIMF in 2020 on “Mathematics in Advanced Medical Imaging: models, algorithms, and big data”. 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 from applied mathematics and medical imaging communities. This is evidenced also by the increasingly large number of international conferences and workshops covering a broad, and diverse range of mathematic topics pertinent to advanced medical imaging, which are well attended by researchers and trainees from both fields of mathematics and medical imaging. We believe that the TSIMF provides a unique setting that facilitates investigators from China and other countries in mathematics and medical imaging fields to share their vision on the research frontiers and to show case cutting-edge research results. In particular. It allows the trainees including graduate students and postdoctoral fellows to be exposed to the new ideas, research, and tools in the fields through attending lectures and interacting with the leading investigators. As the field is fast moving forward, we believe that the proposed topic is both timely and imperative.
One of the outcomes of the workshop is to foster new international collaborations as well as interdisciplinary collaborative projects on mathematical methods and their applications to medical imaging especially within the context of big data. The workshop would also provide an opportunity for young researchers and trainees to be informed of the frontiers in both medical imaging and applied mathematics and learn how to bridge their gaps.
Proposed date and duration.
Our proposed period will be primarily 13-17 Jan 2020 for 2 major reasons: (i) potential participants from North America and Europe are
“free” from teaching in this period shortly after the New Year break, and (ii) as there are a few related events elsewhere towards the end of Jan 2020, this period proposed will ensure no time conflict, thus maximizing the participation of leading investigators as well as the impact of the workshop. For the similar reasons, we can reserve the period of 30 Dec 2019 -3 Jan 2020 as a backup, in case TSIMF is over-subscribed in Jan.
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