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29 July - 4 Aug 2018 Favignana, Sicily Medical Imaging meets Deep Learning |
| MOTIVATION AND DESCRIPTION Medical imaging is the science and technology to acquire images of the human body (either as a whole or in parts) for clinical interpretation or interventions. The main challenge for clinicians lies in the explosive number of images being acquired, and their hidden, often complementary or dynamic information contents. To aid the analysis of this increasing amount and complexity of medical images, medical image computing has emerged as an interdisciplinary field at the interface of computer science, engineering, physics, applied mathematics, and of course medicine. In this field, scientists aim to develop robust and accurate computational methods to extract clinically relevant information. In contrast, the field of deep learning is a subset of machine learning methods in artificial intelligence (AI) that is capable of learning data representations and extracting new features, as opposed to more task-specific algorithms employed in medical imaging that are based on human experience and hand-crafted feature extractions. Recent research in these traditionally separate fields suggests that both scientific communities could mutually benefit from one another – but a scientific gap continues to exist. The focus of this Medical Imaging Summer School (MISS) is to train a new generation of young scientists to bridge this gap, by providing insights into the various interfaces between medical imaging and deep learning, based on the shared broad categories of medical image computing, computer-aided image interpretation and disease classification. The course will contain a combination of in-depth tutorial-style lectures on fundamental state-of-the-art concepts, followed by accessible yet advanced research lectures using examples and applications. A broad overview of the field will be given, and guided reading groups will complement lectures. The course will be delivered by world renowned experts from both academia and industry, who are working closely at the interface of medical imaging/deep learning. The school aims to provide a stimulating graduate training opportunity for young researchers and Ph.D. students. The participants will benefit from direct interaction with world leaders in medical image computing and deep learning(often working in both fields). Participants will also have the opportunity to present their own research, and to interact with their scientific peers, in a friendly and constructive setting. For more information, send an email to miss@dmi.unict.it LIST OF CONFIRMED SPEAKERS
• Michael Bronstein, Università della Svizzera Italiana, Switzerland SCHOOL DIRECTORS •
Roberto
Cipolla, University of Cambridge, United Kingdom SCHOOL APPLICATION The school will be open to about 150 qualified, motivated and pre-selected candidates. Master Students, Ph. D. students, Post-Docs, young researchers (both academic and industrial), senior researchers (both academic and industrial) or academic/industrial professionals are encouraged to apply. The expected school fee will be in the order of 600,00 € for Master and Phd students, 700€ for other academic positions and 900 € for all the others. The fee will include all course materials, coffee/granita breaks, WiFi Internet Connection at the conference centre, welcome cocktail, and other social events. Applications to attend MISS 2018 should be received before 10 April 2018. Applicants will receive notification of acceptance by 15 April 2018. STUDENT POSTERS AND BEST PRESENTATION Accepted students may submit a poster to present their research activity. Suitable space will be reserved to students for showing their posters. The electronic version of the posters will also be available from the MISS web site. A best presentation prize of 600€ (sponsored by CVPL) will be assigned by the school committee. READING GROUP During a typical PhD, students will read probably more than 100 papers. Reading research papers is a skill that can be acquired and that is very different from reading a novel. The reading group sessions are to introduce students to that skill. In small groups of around 10 students per faculty member, students will discuss papers selected by the school faculty. In preparation for this, students are expected to study (not just read) the provided papers in advance, by tracing the ideas in those papers as far back as possible. See reading group detailed instructions. SCHOOL DEADLINES Student
Application: LOCATION OF MISS The school will be held on the beautiful island of Favigana, which is part of the Egadi Archipelago, just 9 miles off the coast Sicily. Favignana is a glorious yet still mostly unspoilt island, which provides excellent conference facilities in form of a former tuna factory – the “Ex Stabilimento Florio”, which used to be the most important industrial plant of the Mediterranean for tuna processing. Today, after having been beautifully restored, its marvellous architecture has made it one of the top sights of Favignana, boasting a museum, an information centre, a large and modern conference room, and very pleasant outside, yet shaded space for poster sessions and breaks. Favignana can easily be reached by sea directly from Trapani (on “mainland Sicily”) via ferry or hydrofoil. Additional hydrofoil services from Naples or Marsala exist. The nearest international airport is Palermo, with some smaller airline carriers flying directly to Trapani. See pictures of previous editions: Set1, Set2. Reduced
room rates for school participants have been arranged and will be offered
on a “first-come first-serve basis” in self-catering appartments
(with kitchen) at single, double, triple or quadruple occupancy, for
330€–650€ per person for 6 nights (depending on occupancy),
including bedding/towels and cleaning. Applications for room reservations
must be made before The accommodation prices for MISS participants are the following:
SCHOOL BUS/FERRY SERVICE The following transfers (including bus + ferry) will be provided by the school (without charge):
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For
more information, send an email to: miss@dmi.unict.it |
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